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  • Distributed frequent subgraph mining on evolving graph using SPARK
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    N. Senthilselvan; V. Subramaniyaswamy; V. Vijayakumar; Hamid Reza Karimi; N. Aswin; Logesh Ravi

    Within the graph mining context, frequent subgraph identification plays a key role in retrieving required information or patterns from the huge amount of data in a short period. The problem of finding frequent items in traditional mining changed to the innovation of subgraphs that recurrently occurs in graph datasets containing a single huge graph. Majority of the existing methods target static graphs

    更新日期:2020-06-30
  • Online analytical processsing on graph data
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    Leticia Gómez; Bart Kuijpers; Alejandro Vaisman

    Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a “Big Data” scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current

    更新日期:2020-06-30
  • Unsupervised learning of textual pattern based on Propagation in Bipartite Graph
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    Thiago de Paulo Faleiros; Alan Valejo; Alneu de Andrade Lopes

    Graph-based algorithms have aroused considerable interests in recent years by facilitating pattern recognition and learning via information propagation process through the graph. Here, we propose an unsupervised learning algorithm based on propagation on bipartite graph, referred to as Propagationin Bipartite Graph (PBG) algorithm. The contributions of this approach are threefold: 1) we present an

    更新日期:2020-06-30
  • Estimating a one-class naive Bayes text classifier
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    Yihong Zhang; Adam Jatowt

    Nowadays more and more information extraction projects need to classify large amounts of text data. The common way to classify text is to build a supervised classifier trained on human-labeled positive and negative examples. In many cases, however, it is easy to label positive examples, but hard tolabel negative examples. In this paper, we address the problem of building a one-class classifier when

    更新日期:2020-06-30
  • An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    Jinkun Luo; Fazhi He; Jiashi Yong

    Bat algorithm (BA) has the advantage of fast convergence, but there is still room for improvement in accuracy and stability of solution. An efficient and robust fusion bat algorithm (ERFBA) is proposed to overcome these defects. In the population reconstruction, an effective diversity population (EDP) is reconstructed by designing a multi-strategy opposition-based learning with disturbance. In the

    更新日期:2020-06-30
  • An active learning ensemble method for regression tasks
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    Nikos Fazakis; Georgios Kostopoulos; Stamatis Karlos; Sotiris Kotsiantis; Kyriakos Sgarbas

    Active learning is a typical approach for learning from both labeled and unlabeled examples aiming to build efficient and accurate predictive models at minimum expense under an expert’s guidance. Since there is a lack of labeled data in many scientific fields whilst, at the same time, the labelingcost of unlabeled data is typically high in terms of time and expenditure, active learning has grown rapidly

    更新日期:2020-06-30
  • An improvement of SAX representation for time series by using complexity invariance
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    Xuan-May Thi Le; Tuan Minh Tran; Hien T. Nguyen

    In the area of time series data mining, a challenging task is to design an effectively and efficiently low-dimensional representation of high-dimensional time series data. Such an effective and efficient representation is important for dimensionality reduction of time series while preserving the core information embedded in the original one. Among popular representations of time series, Symbolic Aggregate

    更新日期:2020-06-30
  • Extraction of qualitative behavior rules for industrial processes from reduced concept lattice
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    Sérgio M. Dias; Luis E. Zárate; Mark A.J. Song; Newton J. Vieira; Ch. Aswani Kumar

    Formal concept analysis (FCA) become an alternative approach to extract and represent knowledge of real world systems. That knowledge can be obtained from implications rules extracted of concept lattices formed by ordered formal concepts. However, in complex systems the number of formal concepts can be large. To deal with this complexity of the FCA, concept reduction techniques can be applied in order

    更新日期:2020-06-30
  • Analyzing concept drift: A case study in the financial sector
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-05-21
    Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón

    In this paper, we present a method for exploratory data analysis of streaming data based on probabilistic graphical models (latent variable models). This method is illustrated by concept drift tracking, using financial client data from a European regional bank. For this particular setting, the analyzed data spans the period from April 2007 to March 2014 and therefore starts before the beginning of

    更新日期:2020-06-30
  • Domain sentiment dictionary construction and optimization based on multi-source information fusion
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Zuo Chen; Xin Li; Min Wang; Shenggang Yang

    Sentiment analysis of text data, such as reviews, can help users and merchants make more favorable decisions. It is difficult to use the popular supervised learning method to complete the sentiment classification task because marking data manually is time-consuming and laborious. Unsupervised sentiment classification methods are mostly based on sentiment lexicons. The existing sentiment lexicons are

    更新日期:2020-03-27
  • A cross-lingual sentiment topic model evolution over time
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Ibrahim Hussein Musa; Kang Xu; Feng Liu; Ibrahim Zamit; Waheed Ahmed Abro; Guilin Qi

    Sentiment analysis in various languages has been a hot research topic with several applications. Most of the existing models have been reported to work well with widely used language. Were the lass directly applying these models to poor-quality corpora often leads to low results. Thus, to deal withthese shortcoming we propose a cross-lingual sentiment topic model evolution over time (CLSTOT) which

    更新日期:2020-03-27
  • A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Katherine C. Kempfert; Yishi Wang; Cuixian Chen; Samuel W.K. Wong

    Because of high dimensionality, correlation among covariates, and noise contained in data, dimension reduction (DR) techniques are often employed to the application of machine learning algorithms. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and their kernel variants (KPCA, KLDA) are among the most popular DR methods. Recently, Supervised Kernel Principal Component Analysis

    更新日期:2020-03-27
  • Hybridization of population-based ant colony optimization via data mining
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Zeynep Adak; Ayhan Demiriz

    We propose a hybrid application of Population Based Ant Colony Optimization that uses a data mining procedure to wisely initialize the pheromone entries. Hybridization of metaheuristics with data mining techniques has been studied by several researchers in recent years. In this line of research, frequent patterns in a number of initial high-quality solutions are extracted to guide the subsequent iterations

    更新日期:2020-03-27
  • An improved opposition based learning firefly algorithm with dragonfly algorithm for solving continuous optimization problems
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Mehdi Abedi; Farhad Soleimanian Gharehchopogh

    Nowadays, the existence of continuous optimization problems has led researchers to come up with a variety of methods to solve continues optimization problems. The metaheuristic algorithms are one of the most popular and common ways to solve continuous optimization problems. Firefly Algorithm (FA) is a successful metaheuristic algorithm for solving continuous optimization problems; however, although

    更新日期:2020-03-27
  • Mining sequences in activities for time use analysis
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Jorge Rosales-Salas; Sebastián Maldonado; Alex Seret

    By providing a complete record of time use for a given population, time use studies enable investigators to test various hypotheses concerning that behavior. However, the large number and variety of activity combinations that are relevant in time allocation choices and, therefore, time use analysis, makes measuring or even fully identifying all of them impossible without the proper data mining tools

    更新日期:2020-03-27
  • An intrusion detection method based on active transfer learning
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Jingmei Li; Weifei Wu; Di Xue

    Intrusion detection plays a very important role in the field of network security. In order to improve the intrusion detection rate, intrusion detection algorithms based traditional machine learning are widely used in this field. These methods generally satisfy the following two assumptions: the training and the testing data must be under the condition of the independent and identical distribution;

    更新日期:2020-03-27
  • Efficient heuristics for learning Bayesian network from labeled and unlabeled data
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Zhiyi Duan; Limin Wang; Minghui Sun

    Bayesian network classifiers (BNCs) are powerful tools to mine statistical knowledge from data and infer under conditions of uncertainty. However, most of the traditional BNCs focus on mining the dependency relationships existed in labeled data while neglecting the information hidden in unlabeled data, which may result in the biased decision boundaries. To address this issue, we introduce a new order-based

    更新日期:2020-03-27
  • Biased transfer matching for less overlapping degree for unsupervised domain adaptation
    Intell. Data Anal. (IF 0.651) Pub Date : 2020-03-27
    Yiran Wen; Xiu Cao; Xueping Wang; Fangyuan Liu

    Domain adaptation is an important branch of transfer learning. Previous studies have always taken efforts to minimize the optimization goal, but they neglect the relative quality of features or instances. For example, a classic work treats different instances equally in a degree and chooses these instances which minimize the optimization function value. This method will discard these instances that

    更新日期:2020-03-27
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