Algorithms for clustering data
Algorithms for clustering data
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
A comparative analysis on the bisecting K-means and the PDDP clustering algorithms
Intelligent Data Analysis
Centrality Measures from Complex Networks in Active Learning
DS '09 Proceedings of the 12th International Conference on Discovery Science
Clustering of time series data-a survey
Pattern Recognition
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Relative clustering validity criteria: A comparative overview
Statistical Analysis and Data Mining
Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
Fast approximate similarity search based on degree-reduced neighborhood graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Cluster detection methods are widely studied in Propositional Data Mining. In this context, data is individually represented as a feature vector. This data has a natural non-relational structure, but can be represented in a relational form through similarity-based network models. In these models, examples are represented by vertices and an edge connects two examples with high similarity. This relational representation allows employing network-based algorithms in Relational Data Mining. Specifically in clustering tasks, these models allow to use community detection algorithms in networks in order to detect data clusters. In this work, we compared traditional non-relational data-based clustering algorithms with clustering detection algorithms based on relational data using measures for community detection in networks. We carried out an exploratory analysis over 23 numerical datasets and 10 textual datasets. Results show that network models can efficiently represent the data topology, allowing their application in cluster detection with higher precision when compared to non-relational methods.