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A clustering ensemble framework based on elite selection of weighted clusters. (English) Zbl 1267.62076

Summary: Each clustering algorithm usually optimizes a qualification metric during its progress. The qualification metric in conventional clustering algorithms considers all the features equally important; in other words each feature participates in the clustering process equivalently. It is obvious that some features have more information than others in a data set. So it is highly likely that some features should have lower importance degrees during a clustering or a classification algorithm; due to their lower information or their higher variances, etc. So it is always a desire for all artificial intelligence communities to enforce the weighting mechanism in any task that identically uses a number of features to make a decision. But there is always a certain problem of how the features can be participated in the clustering process (in any algorithm, but especially in clustering algorithm) in a weighted manner. Recently, this problem is dealt with by locally adaptive clustering (LAC). However, like its traditional competitors the LAC suffers from inefficiency in data with imbalanced clusters. This paper solves the problem by proposing a weighted locally adaptive clustering (WLAC) algorithm that is based on the LAC algorithm. However, WLAC algorithm suffers from sensitivity to its two parameters that should be tuned manually. The performance of WLAC algorithm is affected by well-tuning of its parameters. This paper proposes two solutions. The first is based on a simple clustering ensemble framework to examine the sensitivity of the WLAC algorithm to its manual well-tuning. The second is based on cluster selection method.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
65C60 Computational problems in statistics (MSC2010)

Software:

UCI-ml
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References:

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