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In comparison with hard clustering methods, in which a pattern belongs to a single cluster, fuzzy clustering algorithms allow patterns to belong to all clusters with differing degrees of membership. This is important in domains such as sentence clustering, since a sentence is likely to be related to more than one theme or topic present within a document or set of documents. However, because most sentence similarity measures do not represent sentences in a common metric space, conventional fuzzy clustering approaches based on prototypes or mixtures of Gaussians are generally not applicable to sentence clustering. This paper presents a novel fuzzy clustering algorithm that operates on relational input data; i.e., data in the form of a square matrix of pairwise similarities between data objects. The algorithm uses a graph representation of the data, and operates in an Expectation-Maximization framework in which the graph centrality of an object in the graph is interpreted as a likelihood. Results of applying the algorithm to sentence clustering tasks demonstrate that the algorithm is capable of identifying overlapping clusters of semantically related sentences, and that it is therefore of potential use in a variety of text mining tasks. We also include results of applying the algorithm to benchmark data sets in several other domains. [ABSTRACT FROM AUTHOR]

This paper describes Olex, a novel method for the automatic induction of rule-based text classifiers. Olex supports a hypothesis language of the form "if T1 or … or Tn occurs in document d, and none of Tn+1,…Tn+m occurs in d, then classify d under category c," where each Ti is a conjunction of terms. The proposed method is simple and elegant. Despite this, the results of a systematic experimentation performed on the REUTERS-21578, the ORSUMED, and the ODP data collections show that Olex provides classifiers that are accurate, compact, and comprehensible. A comparative analysis conducted against some of the most well-known learning algorithms (namely, Naive Bayes, Ripper, C4.5, SVM, and Linear Logistic Regression) demonstrates that it is more than competitive in terms of both predictive accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Web users' clustering is a crucial task for mining information related to users' needs and preferences. Up to now, popular clustering approaches build clusters based on usage patterns derived from users' page preferences. This paper emphasizes the need to discover similarities in users' accessing behavior with respect to the time locality of their navigational acts. In this context, we present two time-aware clustering approaches for tuning and binding the page and time visiting criteria. The two tracks of the proposed algorithms define clusters with users that show similar visiting behavior at the same time period, by varying the priority given to page or time visiting. The proposed algorithms are evaluated using both synthetic and real data sets and the experimentation has shown that the new clustering schemes result in enriched clusters compared to those created by the conventional non-time-aware user clustering approaches. These clusters contain users exhibiting similar access behavior in terms not only of their page preferences but also of their access time. [ABSTRACT FROM AUTHOR]

Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. In this paper, we propose a new supervised feature selection method, named CHIR, which is based on the X2 statistic and new statistical data that can measure the positive term-category dependency. We a so propose a new text clustering algorithm, named Text Clustering with Feature Selection (TCFS). TCFS can incorporate CHIR to identity relevant features (i.e., terms) iteratively, and the clustering becomes a learning process. We compared TCFS and the K-means clustering algorithm in combination with different feature selection methods for various real data sets. Our experimental results show that TCFS with CHIR has better clustering accuracy in terms of the F-measure and the purity. [ABSTRACT FROM AUTHOR]

Incorporating constraints into frequent itemset mining not only improves data mining efficiency, but also leads to concise and meaningful results. In this paper, a framework for closed constrained gradient itemset mining in retail databases is proposed by introducing the concept of gradient constraint into closed itemset mining. A tailored version of CLOSET+, LCLOSET, is first briefly introduced, which is designed for efficient closed itemset mining from sparse databases. Then, a newly proposed weaker but antimonotone measure, top-X average measure, is proposed and can be adopted to prune search space effectively. Experiments show that a combination of LCLOSET and the top-X average pruning provides an efficient approach to mining frequent closed gradient itemsets. [ABSTRACT FROM AUTHOR]