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Open AccessArticle

Improving CLOPE’s Profit Value and Stability with an Optimized Agglomerative Approach

by 1,2,*, 2 and 2
1
School of Information Science and Technology, Donghua University, Shanghai 201620, China
2
School of Computer Science and Technology, Donghua University, Shanghai 201620, China
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Algorithms 2015, 8(3), 380-394; https://doi.org/10.3390/a8030380
Received: 4 May 2015 / Revised: 12 June 2015 / Accepted: 12 June 2015 / Published: 26 June 2015
(This article belongs to the Special Issue Clustering Algorithms)
CLOPE (Clustering with sLOPE) is a simple and fast histogram-based clustering algorithm for categorical data. However, given the same data set with the same input parameter, the clustering results by this algorithm would possibly be different if the transactions are input in a different sequence. In this paper, a hierarchical clustering framework is proposed as an extension of CLOPE to generate stable and satisfactory clustering results based on an optimized agglomerative merge process. The new clustering profit is defined as the merge criteria and the cluster graph structure is proposed to optimize the merge iteration process. The experiments conducted on two datasets both demonstrate that the agglomerative approach achieves stable clustering results with a better profit value, but costs much more time due to the worse complexity. View Full-Text
Keywords: CLOPE; categorical data; optimized agglomerative approach; cluster graph CLOPE; categorical data; optimized agglomerative approach; cluster graph
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Li, Y.; Le, J.; Wang, M. Improving CLOPE’s Profit Value and Stability with an Optimized Agglomerative Approach. Algorithms 2015, 8, 380-394.

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