A Tolerance Rough Set Based Overlapping Clustering for the DBLP Data

Obadi, Gamila; Dráždilová, Pavla; Hlavacek, Lukas; Martinovič, Jan; Snášel, Václav

doi:10.1109/wi-iat.2010.286

Cited by 11 publications

(3 citation statements)

References 18 publications

(15 reference statements)

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“…The overlapping clustering problem experienced extensive growth since it was introduced in 1971 by Jardine and Sibson in [11]. One of the most popular directions of constructing overlapping clustering is formulated as a graph decomposition problem that was studied in such papers as [1,12], where authors solve the problem of minimization graph's conductance, [16] determines overlapping network module hierarchy, [10] finds overlapping communities in networks, or [14] that presents hierarchical clustering algorithm. The next important group of overlapping clustering methods is based on the probabilistic approach that was studied in such papers as [13] that presents the Naive Bayes Model, [3] that proposes the probabilistic relational models (PRMs), [2,9] generalizes mixture model method to any other exponential distribution, [15] presents the Multiple Cause Mixture Model.…”

Section: Prior Workmentioning

confidence: 99%

Discovering the Graph Structure in Clustering Results

Bauman¹,

Bauman

2018

Advances in Intelligent Systems and Computing

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In a standard cluster analysis, such as k-means, in addition to clusters locations and distances between them, it's important to know if they are connected or well separated from each other. The main focus of this paper is discovering the relations between the resulting clusters. We propose a new method which is based on pairwise overlapping k-means clustering, that in addition to means of clusters provides the graph structure of their relations. The proposed method has a set of parameters that can be tuned in order to control the sensitivity of the model and the desired relative size of the pairwise overlapping interval between means of two adjacent clusters, i.e., level of overlapping. We present the exact formula for calculating that parameter. The empirical study presented in the paper demonstrates that our approach works well not only on toy data but also compliments standard clustering results with a reasonable graph structure on real datasets, such as financial indices and restaurants.

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Section: Prior Workmentioning

confidence: 99%

Discovering the Graph Structure in Clustering Results

Bauman¹,

Bauman

2018

Advances in Intelligent Systems and Computing

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“…Aydin et al [5] proposed an overlapping clustering algorithm used in Ad hoc networks, it can improve network reliability and load balancing. Specific to the DBLP data, Obadi et al [6] introduced an overlapping clustering method, which can solve the problem that a paper may corresponding to multiple topics. Lingras et al [7] compared crisp and fuzzy clustering in the mobile phone call dataset, and pointed out that, fuzzy clustering can capture objects which will split into two or more clusters form a single cluster when the number of clusters are increased.…”

Section: Introductionmentioning

confidence: 99%

Categorizing Overlapping Regions in Clustering Analysis Using Three-Way Decisions

Jiao

Wang

et al. 2014

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Clustering is a common technique for data analysis, has been widely used in many practical area. In many real applications such as social network analysis, wireless sensor networks, document clustering, and so on, there exist overlaps between different clusters due to various reasons. In this paper, we propose to use the three-way decisions approach to address categorizing overlapping regions. In contrast to existing soft clustering methods that just point out the objects whether in overlapping regions, the three-way decisions method provides a greater refinement of the categorization to system operators for further analysis, which is believed to show clearly the objects have different impacts to construct clusters. Besides, we provide a new relation-graph based clustering algorithm to obtain different overlapping region types. The results of comparison experiments are better and more reasonable to overlapping clustering.

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“…It is successfully applied in various tasks, the selection / attribute extraction, synthesis and classification rules, knowledge discovery, etc. Tolerance rough set model employing a tolerance relation is not an eqivalence relationship in the original model of rough sets [3].…”

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confidence: 99%