Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.

中文翻译：

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基于集合理论的文本聚类和分类相似度度量

长期以来，相似性度量已在信息检索和机器学习领域中用于多种用途，包括文本检索，文本聚类，文本摘要，抄袭检测以及其他几种文本处理应用程序。但是，这些措施的问题在于，直到最近，还没有任何一项措施被记录为能够同时具有很高的效率和效率。因此，寻求有效和有效的相似性度量仍然是一个开放的挑战。因此，这项研究为文本聚类和分类引入了一种新的高效且省时的相似性度量。此外，该研究旨在为七个最广泛使用的相似性度量提供全面的审查，主要涉及其有效性和效率。使用K最近邻算法（KNN）进行分类，K-means聚类算法以及词袋（BoW）模型进行特征选择，所有相似性度量均经过仔细检查。实验评估是对两个最受欢迎的数据集，即Reuters-21和Web-KB进行的。获得的结果证实，所提出的基于集合论的相似性度量（STB-SM）作为一种杰出度量，在有效性和效率方面都大大超过了所有现有技术度量。