Comparing relational and non-relational algorithms for clustering propositional data

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Motta, R (Author) Other; Nogueira, BM (Author) Other; Jorge, AM (Author) FCUP; De Andrade Lopes, A (Author) Other; Rezende, SO (Author) Other; De Oliveira, MCF (Author) Other

Conference proceedings International

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 150-155

28th Annual ACM Symposium on Applied Computing, SAC 2013

Coimbra, 18 March 2013 through 22 March 2013

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-008-B2K

DOI: 10.1145/2480362.2480393

Abstract (EN): 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 nonrelational 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. Copyright 2013 ACM.

Language: English

Type (Professor's evaluation): Scientific