A new selection strategy for selective cluster ensemble based on Diversity and Independency. (November 2016)

• Record Type: Journal Article
• Authors: Yousefnezhad, Muhammad; Reihanian, Ali; Zhang, Daoqiang; Minaei-Bidgoli, Behrouz
• Is Part Of: Engineering applications of artificial intelligence. Volume 56(2016:Aug.)
• Page Start: 260
• Page End: 272
• Publication Date: 2016-11
• Subjects: Independency of algorithms -- Diversity of primary results -- Selective cluster ensemble -- Algorithm's graph; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.10.005
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This research introduces a new strategy in cluster ensemble selection by using Independency and Diversity metrics. In recent years, Diversity and Quality, which are two metrics in evaluation procedure, have been used for selecting basic clustering results in the cluster ensemble selection. Although quality can improve the final results in cluster ensemble, it cannot control the procedures of generating basic results, which causes a gap in prediction of the generated basic results' accuracy. Instead of quality, this paper introduces Independency as a supplementary method to be used in conjunction with Diversity. Therefore, this paper uses a heuristic metric, which is based on the procedure of converting code to graph in Software Testing, in order to calculate the Independency of two basic clustering algorithms. Moreover, a new modeling language, which we called as "Clustering Algorithms Independency Language" (CAIL), is introduced in order to generate graphs which depict Independency of algorithms. Also, Uniformity, which is a new similarity metric, has been introduced for evaluating the diversity of basic results. As a credential, our experimental results on varied different standard data sets show that the proposed framework improves the accuracy of final results dramatically in comparison with other cluster ensemble methods.