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  • Challenges in benchmarking stream learning algorithms with real-world data
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-07-07
    Vinicius M. A. Souza, Denis M. dos Reis, André G. Maletzke, Gustavo E. A. P. A. Batista

    Streaming data are increasingly present in real-world applications such as sensor measurements, satellite data feed, stock market, and financial data. The main characteristics of these applications are the online arrival of data observations at high speed and the susceptibility to changes in the data distributions due to the dynamic nature of real environments. The data stream mining community still

  • Visualizing image content to explain novel image discovery
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-07-06
    Jake H. Lee, Kiri L. Wagstaff

    The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends. We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the data set or, potentially, the discovery of previously unknown phenomena. To aid in interpreting

  • Credible seed identification for large-scale structural network alignment
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-07-03
    Chenxu Wang, Yang Wang, Zhiyuan Zhao, Dong Qin, Xiapu Luo, Tao Qin

    Structural network alignment utilizes the topological structure information to find correspondences between nodes of two networks. Researchers have proposed a line of useful algorithms which usually require a prior mapping of seeds acting as landmark points to align the rest nodes. Several seed-free algorithms are developed to solve the cold-start problem. However, existing approaches suffer high computational

  • Introducing time series snippets: a new primitive for summarizing long time series
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-07-02
    Shima Imani, Frank Madrid, Wei Ding, Scott E. Crouter, Eamonn Keogh

    The first question a data analyst asks when confronting a new dataset is often, “Show me some representative/typical data.” Answering this question is simple in many domains, with random samples or aggregate statistics of some kind. Surprisingly, it is difficult for large time series datasets. The major difficulty is not time or space complexity, but defining what it means to be representative data

  • Gaussian bandwidth selection for manifold learning and classification
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-07-02
    Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch

    Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel’s scale parameter, also referred to as the kernel’s bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification

  • Large-scale network motif analysis using compression
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-23
    Peter Bloem, Steven de Rooij

    We introduce a new method for finding network motifs. Subgraphs are motifs when their frequency in the data is high compared to the expected frequency under a null model. To compute this expectation, a full or approximate count of the occurrences of a motif is normally repeated on as many as 1000 random graphs sampled from the null model; a prohibitively expensive step. We use ideas from the minimum

  • Treant : training evasion-aware decision trees
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-21
    Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei, Seyum Assefa Abebe, Salvatore Orlando

    Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i.e., carefully crafted perturbations of test inputs designed to force prediction errors. In this paper we focus on evasion attacks against decision tree ensembles, which are among the most successful predictive models for dealing with non-perceptual problems. Even though they are powerful and interpretable

  • Scalable attack on graph data by injecting vicious nodes
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-17
    Jihong Wang, Minnan Luo, Fnu Suya, Jundong Li, Zijiang Yang, Qinghua Zheng

    Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph with unnoticeable perturbations. However, a vast majority of existing works cannot handle large-scale graphs because of their high time complexity. Additionally, existing works mainly focus on manipulating existing nodes

  • Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-16
    Hannah R. Kerner, Kiri L. Wagstaff, Brian D. Bue, Danika F. Wellington, Samantha Jacob, Paul Horton, James F. Bell, Chiman Kwan, Heni Ben Amor

    Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up observations. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and focus attention on the most promising or novel observations. Several novelty

  • TEASER: early and accurate time series classification
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-16
    Patrick Schäfer, Ulf Leser

    Early time series classification (eTSC) is the problem of classifying a time series after as few measurements as possible with the highest possible accuracy. The most critical issue of any eTSC method is to decide when enough data of a time series has been seen to take a decision: Waiting for more data points usually makes the classification problem easier but delays the time in which a classification

  • Efficient mining of the most significant patterns with permutation testing
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-09
    Leonardo Pellegrina, Fabio Vandin

    The extraction of patterns displaying significant association with a class label is a key data mining task with wide application in many domains. We introduce and study a variant of the problem that requires to mine the top-k statistically significant patterns, thus providing tight control on the number of patterns reported in output. We develop TopKWY, the first algorithm to mine the top-k significant

  • ColluEagle: collusive review spammer detection using Markov random fields
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-06
    Zhuo Wang, Runlong Hu, Qian Chen, Pei Gao, Xiaowei Xu

    Product reviews are extremely valuable for online shoppers in providing purchase decisions. Driven by immense profit incentives, fraudsters deliberately fabricate untruthful reviews to distort the reputation of online products. As online reviews become more and more important, group spamming, i.e., a team of fraudsters working collaboratively to attack a set of target products, becomes a new fashion

  • TEAGS: time-aware text embedding approach to generate subgraphs
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-03
    Saeid Hosseini, Saeed Najafipour, Ngai-Man Cheung, Hongzhi Yin, Mohammad Reza Kangavari, Xiaofang Zhou

    Contagions (e.g. virus and gossip) spread over the nodes in propagation graphs. We can use temporal-textual contents of nodes to compute the edge weights and generate subgraphs with highly relevant nodes. This is beneficial to many applications. Yet, challenges abound. First, the propagation pattern between each pair of nodes may change by time. Second, not always the same contagion propagates. Hence

  • ABBA: adaptive Brownian bridge-based symbolic aggregation of time series
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-06-03
    Steven Elsworth, Stefan Güttel

    A new symbolic representation of time series, called ABBA, is introduced. It is based on an adaptive polygonal chain approximation of the time series into a sequence of tuples, followed by a mean-based clustering to obtain the symbolic representation. We show that the reconstruction error of this representation can be modelled as a random walk with pinned start and end points, a so-called Brownian

  • An ultra-fast time series distance measure to allow data mining in more complex real-world deployments
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-05-30
    Shaghayegh Gharghabi, Shima Imani, Anthony Bagnall, Amirali Darvishzadeh, Eamonn Keogh

    At their core, many time series data mining algorithms reduce to reasoning about the shapes of time series subsequences. This requires an effective distance measure, and for last two decades most algorithms use Euclidean distance or DTW as their core subroutine. We argue that these distance measures are not as robust as the community seems to believe. The undue faith in these measures perhaps derives

  • struc2gauss : Structural role preserving network embedding via Gaussian embedding
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-05-12
    Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy

    Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information

  • Matrix profile goes MAD: variable-length motif and discord discovery in data series
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-05-07
    Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn Keogh

    In the last 15 years, data series motif and discord discovery have emerged as two useful and well-used primitives for data series mining, with applications to many domains, including robotics, entomology, seismology, medicine, and climatology. Nevertheless, the state-of-the-art motif and discord discovery tools still require the user to provide the relative length. Yet, in several cases, the choice

  • Counting frequent patterns in large labeled graphs: a hypergraph-based approach
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-05-05
    Jinghan Meng, Napath Pitaksirianan, Yi-Cheng Tu

    In recent years, the popularity of graph databases has grown rapidly. This paper focuses on single-graph as an effective model to represent information and its related graph mining techniques. In frequent pattern mining in a single-graph setting, there are two main problems: support measure and search scheme. In this paper, we propose a novel framework for designing support measures that brings together

  • Discrete-time survival forests with Hellinger distance decision trees
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-03-14
    Matthias Schmid, Thomas Welchowski, Marvin N. Wright, Moritz Berger

    Random survival forests (RSF) are a powerful nonparametric method for building prediction models with a time-to-event outcome. RSF do not rely on the proportional hazards assumption and can be readily applied to both low- and higher-dimensional data. A remaining limitation of RSF, however, arises from the fact that the method is almost entirely focussed on continuously measured event times. This issue

  • Relaxing the strong triadic closure problem for edge strength inference
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-01-17
    Florian Adriaens, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt, Antonis Matakos, Polina Rozenshtein

    Social networks often provide only a binary perspective on social ties: two individuals are either connected or not. While sometimes external information can be used to infer the strength of social ties, access to such information may be restricted or impractical to obtain. Sintos and Tsaparas (KDD 2014) first suggested to infer the strength of social ties from the topology of the network alone, by

  • ptype: probabilistic type inference
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-03-16
    Taha Ceritli, Christopher K. I. Williams, James Geddes

    Type inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype, a probabilistic robust type inference method that allows us to detect such entries, and infer data types. We further show that the proposed method outperforms

  • An efficient K -means clustering algorithm for tall data
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-03-10
    Marco Capó, Aritz Pérez, Jose A. Lozano

    The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. Therefore, the development of efficient and parallel algorithms to perform such an analysis is a a crucial topic in unsupervised learning. Cluster analysis algorithms are a key element of exploratory data analysis and, among them, the K-means algorithm stands out as the most popular approach

  • Robust and sparse multigroup classification by the optimal scoring approach
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-02-20
    Irene Ortner, Peter Filzmoser, Christophe Croux

    We propose a robust and sparse classification method based on the optimal scoring approach. It is also applicable if the number of variables exceeds the number of observations. The data are first projected into a low dimensional subspace according to an optimal scoring criterion. The projection only includes a subset of the original variables (sparse modeling) and is not distorted by outliers (robust

  • MasterMovelets: discovering heterogeneous movelets for multiple aspect trajectory classification
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-01-30
    Carlos Andres Ferrero, Lucas May Petry, Luis Otavio Alvares, Camila Leite da Silva, Willian Zalewski, Vania Bogorny

    In the last few years trajectory classification has been applied to many real problems, basically considering the dimensions of space and time or attributes inferred from these dimensions. However, with the explosion of social media data and the advances in the semantic enrichment of mobility data, a new type of trajectory data has emerged, and the trajectory spatio-temporal points have now multiple

  • Computing exact P-values for community detection
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-03-16
    Zengyou He, Hao Liang, Zheng Chen, Can Zhao, Yan Liu

    Community detection is one of the most important issues in modern network science. Although numerous community detection algorithms have been proposed during the past decades, how to assess the statistical significance of one single community analytically and exactly still remains an open problem. In this paper, we present an analytical solution to calculate the exact p-value of a single community

  • TS-CHIEF: a scalable and accurate forest algorithm for time series classification
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-03-05
    Ahmed Shifaz, Charlotte Pelletier, François Petitjean, Geoffrey I. Webb

    Time Series Classification (TSC) has seen enormous progress over the last two decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is the current state of the art in terms of classification accuracy. HIVE-COTE recognizes that time series data are a specific data type for which the traditional attribute-value representation, used predominantly in machine learning, fails

  • Model-based exception mining for object-relational data
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-02-19
    Fatemeh Riahi, Oliver Schulte

    This paper develops model-based exception mining and outlier detection for the case of object-relational data. Object-relational data represent a complex heterogeneous network, which comprises objects of different types, links among these objects, also of different types, and attributes of these links. We follow the well-established exceptional model mining (EMM) framework, which has been previously

  • The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-03-31
    Yan Zhu, Shaghayegh Gharghabi, Diego Furtado Silva, Hoang Anh Dau, Chin-Chia Michael Yeh, Nader Shakibay Senobari, Abdulaziz Almaslukh, Kaveh Kamgar, Zachary Zimmerman, Gareth Funning, Abdullah Mueen, Eamonn Keogh

    The recently introduced data structure, the Matrix Profile, annotates a time series by recording the location of and distance to the nearest neighbor of every subsequence. This information trivially provides answers to queries for both time series motifs and time series discords, perhaps two of the most frequently used primitives in time series data mining. One attractive feature of the Matrix Profile

  • Guided sampling for large graphs
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-03-18
    Muhammad Irfan Yousuf, Suhyun Kim

    Large real-world graphs claim lots of resources in terms of memory and computational power to study them and this makes their full analysis extremely challenging. In order to understand the structure and properties of these graphs, we intend to extract a small representative subgraph from a big graph while preserving its topology and characteristics. In this work, we aim at producing good samples with

  • A survey and benchmarking study of multitreatment uplift modeling
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-01-13
    Diego Olaya, Kristof Coussement, Wouter Verbeke

    Uplift modeling is an instrument used to estimate the change in outcome due to a treatment at the individual entity level. Uplift models assist decision-makers in optimally allocating scarce resources. This allows the selection of the subset of entities for which the effect of a treatment will be largest and, as such, the maximization of the overall returns. The literature on uplift modeling mostly

  • On normalization and algorithm selection for unsupervised outlier detection
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-11-21
    Sevvandi Kandanaarachchi, Mario A. Muñoz, Rob J. Hyndman, Kate Smith-Miles

    This paper demonstrates that the performance of various outlier detection methods is sensitive to both the characteristics of the dataset, and the data normalization scheme employed. To understand these dependencies, we formally prove that normalization affects the nearest neighbor structure, and density of the dataset; hence, affecting which observations could be considered outliers. Then, we perform

  • SIAS-miner: mining subjectively interesting attributed subgraphs
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-11-22
    Anes Bendimerad, Ahmad Mel, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie

    Data clustering, local pattern mining, and community detection in graphs are three mature areas of data mining and machine learning. In recent years, attributed subgraph mining has emerged as a new powerful data mining task in the intersection of these areas. Given a graph and a set of attributes for each vertex, attributed subgraph mining aims to find cohesive subgraphs for which (some of) the attribute

  • Parameterized low-rank binary matrix approximation
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-01-02
    Fedor V. Fomin, Petr A. Golovach, Fahad Panolan

    Low-rank binary matrix approximation is a generic problem where one seeks a good approximation of a binary matrix by another binary matrix with some specific properties. A good approximation means that the difference between the two matrices in some matrix norm is small. The properties of the approximation binary matrix could be: a small number of different columns, a small binary rank or a small Boolean

  • Integer programming ensemble of temporal relations classifiers
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-01-02
    Catherine Kerr, Terri Hoare, Paula Carroll, Jakub Mareček

    The extraction of temporal events from text and the classification of temporal relations among both temporal events and time expressions are major challenges for the interface of data mining and natural language processing. We present an ensemble method, which reconciles the outputs of multiple heterogenous classifiers of temporal expressions. We use integer programming, a constrained optimisation

  • Mining relaxed functional dependencies from data
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-12-23
    Loredana Caruccio, Vincenzo Deufemia, Giuseppe Polese

    Relaxed functional dependencies (rfds) are properties expressing important relationships among data. Thanks to the introduction of approximations in data comparison and/or validity, they can capture constraints useful for several purposes, such as the identification of data inconsistencies or patterns of semantically related data. Nevertheless, rfds can provide benefits only if they can be automatically

  • NegPSpan: efficient extraction of negative sequential patterns with embedding constraints
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-01-21
    Thomas Guyet, René Quiniou

    Sequential pattern mining is concerned with the extraction of frequent or recurrent behaviors, modeled as subsequences, from a sequence dataset. Such patterns inform about which events are frequently observed in sequences, i.e. events that really happen. Sometimes, knowing that some specific event does not happen is more informative than extracting observed events. Negative sequential patterns (NSPs)

  • Identifying exceptional (dis)agreement between groups
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-11-26
    Adnene Belfodil, Sylvie Cazalens, Philippe Lamarre, Marc Plantevit

    Under the term behavioral data, we consider any type of data featuring individuals performing observable actions on entities. For instance, voting data depict parliamentarians who express their votes w.r.t. legislative procedures. In this work, we address the problem of discovering exceptional (dis)agreement patterns in such data, i.e., groups of individuals that exhibit an unexpected (dis)agreement

  • Fair-by-design matching
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-02-04
    David García-Soriano, Francesco Bonchi

    Matching algorithms are used routinely to match donors to recipients for solid organs transplantation, for the assignment of medical residents to hospitals, record linkage in databases, scheduling jobs on machines, network switching, online advertising, and image recognition, among others. Although many optimal solutions may exist to a given matching problem, when the elements that shall or not be

  • Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2020-01-29
    Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy

    Abstract Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns

  • Topical network embedding
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-10-24
    Min Shi, Yufei Tang, Xingquan Zhu, Jianxun Liu, Haibo He

    Networked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the citation relationships in scholarly publications where a paper is cited by others not because they have the same content, but because they share one or multiple

  • Grafting for combinatorial binary model using frequent itemset mining
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-10-28
    Taito Lee, Shin Matsushima, Kenji Yamanishi

    Abstract We consider the class of linear predictors over all logical conjunctions of binary attributes, which we refer to as the class of combinatorial binary models (CBMs) in this paper. CBMs are of high knowledge interpretability but naïve learning of them from labeled data requires exponentially high computational cost with respect to the length of the conjunctions. On the other hand, in the case

  • Interactive visual data exploration with subjective feedback: an information-theoretic approach
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-10-03
    Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie

    Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing projection methods for data visualization use predefined criteria to choose the representation of data. There is a lack of methods that (i) use information on what the user has learned from the data and (ii) show patterns that she does not know yet. We construct a theoretical

  • A comparative study of data-dependent approaches without learning in measuring similarities of data objects
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-10-30
    Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari

    Abstract Conventional general-purpose distance-based similarity measures, such as Minkowski distance (also known as \(\ell _p\)-norm with \(p>0\)), are data-independent and sensitive to units or scales of measurement. There are existing general-purpose data-dependent measures, such as rank difference, Lin’s probabilistic measure and \(m_p\)-dissimilarity (\(p>0\)), which are not sensitive to units

  • A semi-supervised model for knowledge graph embedding
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-09-24
    Jia Zhu, Zetao Zheng, Min Yang, Gabriel Pui Cheong Fung, Yong Tang

    Knowledge graphs have shown increasing importance in broad applications such as question answering, web search, and recommendation systems. The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces to perform various machine learning tasks. Most of the existing works only focused on the local structure of knowledge

  • Matching code and law: achieving algorithmic fairness with optimal transport
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-11-01
    Meike Zehlike, Philipp Hacker, Emil Wiedemann

    Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the continuous fairness algorithm \((\hbox {CFA}\theta )\) which enables a continuous interpolation between different fairness definitions. More specifically,

  • A drift detection method based on dynamic classifier selection
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-10-11
    Felipe Pinagé, Eulanda M. dos Santos, João Gama

    Abstract Machine learning algorithms can be applied to several practical problems, such as spam, fraud and intrusion detection, and customer preferences, among others. In most of these problems, data come in streams, which mean that data distribution may change over time, leading to concept drift. The literature is abundant on providing supervised methods based on error monitoring for explicit drift

  • FastEE: Fast Ensembles of Elastic Distances for time series classification
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-11-18
    Chang Wei Tan, François Petitjean, Geoffrey I. Webb

    Abstract In recent years, many new ensemble-based time series classification (TSC) algorithms have been proposed. Each of them is significantly more accurate than their predecessors. The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is currently the most accurate TSC algorithm when assessed on the UCR repository. It is a meta-ensemble of 5 state-of-the-art ensemble-based

  • Delayed labelling evaluation for data streams
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-11-16
    Maciej Grzenda, Heitor Murilo Gomes, Albert Bifet

    Abstract A large portion of the stream mining studies on classification rely on the availability of true labels immediately after making predictions. This approach is well exemplified by the test-then-train evaluation, where predictions immediately precede true label arrival. However, in many real scenarios, labels arrive with non-negligible latency. This raises the question of how to evaluate classifiers

  • Deep multi-task learning for individuals origin–destination matrices estimation from census data
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-11-12
    Mehdi Katranji, Sami Kraiem, Laurent Moalic, Guilhem Sanmarty, Ghazaleh Khodabandelou, Alexandre Caminada, Fouad Hadj Selem

    Abstract Rapid urbanization has made the estimation of the human mobility flows a substantial task for transportation and urban planners. Worker and student mobility flows are among the most weekly regular displacements and consequently generate road congestion issues. With urge of demands on efficient transport planning policies, estimating their commuting facilitates the decision-making processes

  • Correction to: Domain agnostic online semantic segmentation for multi-dimensional time series
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-02-14
    Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn Keogh

    The article Domain agnostic online semantic segmentation for multi-dimensional time series, written by Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn Keogh was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 25 September 2018 without open access.

  • Efficient mixture model for clustering of sparse high dimensional binary data
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-06-01
    Marek Śmieja, Krzysztof Hajto, Jacek Tabor

    Clustering is one of the fundamental tools for preliminary analysis of data. While most of the clustering methods are designed for continuous data, sparse high-dimensional binary representations became very popular in various domains such as text mining or cheminformatics. The application of classical clustering tools to this type of data usually proves to be very inefficient, both in terms of computational

  • catch22 : CAnonical Time-series CHaracteristics
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-08-09
    Carl H. Lubba, Sarab S. Sethi, Philip Knaute, Simon R. Schultz, Ben D. Fulcher, Nick S. Jones

    Abstract Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library

  • A unifying view of explicit and implicit feature maps of graph kernels
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-09-17
    Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel

    Abstract Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. We investigate how convolution kernels for structured data are composed from base kernels and construct corresponding feature maps. On this basis we propose exact and approximative feature maps for widely used graph kernels based on the

  • A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-06-17
    James Large, Jason Lines, Anthony Bagnall

    Abstract Our hypothesis is that building ensembles of small sets of strong classifiers constructed with different learning algorithms is, on average, the best approach to classification for real-world problems. We propose a simple mechanism for building small heterogeneous ensembles based on exponentially weighting the probability estimates of the base classifiers with an estimate of the accuracy formed

  • SAZED: parameter-free domain-agnostic season length estimation in time series data
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-07-26
    Maximilian Toller, Tiago Santos, Roman Kern

    Abstract Season length estimation is the task of identifying the number of observations in the dominant repeating pattern of seasonal time series data. As such, it is a common pre-processing task crucial for various downstream applications. Inferring season length from a real-world time series is often challenging due to phenomena such as slightly varying period lengths and noise. These issues may

  • Extending inverse frequent itemsets mining to generate realistic datasets: complexity, accuracy and emerging applications
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-07-20
    Domenico Saccá, Edoardo Serra, Antonino Rullo

    Abstract The development of novel platforms and techniques for emerging “Big Data” applications requires the availability of real-life datasets for data-driven experiments, which are however not accessible in most cases for various reasons, e.g., confidentiality, privacy or simply insufficient availability. An interesting solution to ensure high quality experimental findings is to synthesize datasets

  • Contextual bandits with hidden contexts: a focused data capture from social media streams
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-08-10
    Sylvain Lamprier, Thibault Gisselbrecht, Patrick Gallinari

    This paper addresses the problem of real time data capture from social media. Due to different limitations, it is not possible to collect all the data produced by social networks such as Twitter. Therefore, to be able to gather enough relevant information related to a predefined need, it is necessary to focus on a subset of the information sources. In this work, we focus on user-centered data capture

  • Attributed network embedding via subspace discovery
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-08-26
    Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

    Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network

  • Dynamics reconstruction and classification via Koopman features
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-06-24
    Wei Zhang, Yao-Chi Yu, Jr-Shin Li

    Abstract Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and biology to medicine. Tools from machine learning, data mining, and neurocomputing have been extensively explored and utilized to accomplish such compelling data analytics tasks. However, for time-series data presenting active dynamic characteristics

  • Wrangling messy CSV files by detecting row and type patterns
    Data Min. Knowl. Discov. (IF 2.629) Pub Date : 2019-07-26
    G. J. J. van den Burg, A. Nazábal, C. Sutton

    Abstract Data scientists spend the majority of their time on preparing data for analysis. One of the first steps in this preparation phase is to load the data from the raw storage format. Comma-separated value (CSV) files are a popular format for tabular data due to their simplicity and ostensible ease of use. However, formatting standards for CSV files are not followed consistently, so each file requires

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