An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA
Fecha
2020-01-01
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Institute of Electrical and Electronics Engineers Inc.
Resumen
Clustering is a research challenge focused on discovering knowledge from data samples whose goal is to build good quality partitions. In this paper is proposed an approach based on LAMDA (Learning Algorithm for Multivariable Data Analysis), whose most important features are: a) it is a non-iterative fuzzy algorithm that can work with online data streams, b) it does not require the number of clusters, c) it can generate new partitions with objects that do not have enough similarity with the preexisting clusters (incremental-learning). However, in some applications, the number of created partitions does not correspond with the number of desired clusters, which can be excessive or impractical for the expert. Therefore, our contribution is the formalization of an automatic merge technique to update the cluster partition performed by LAMDA to improve the quality of the clusters, and a new methodology to compute the Marginal Adequacy Degree that enhances the individual-cluster assignment. The proposal, called LAMDA-RD, is applied to several benchmarks, comparing the results against the original LAMDA and other clustering algorithms, to evaluate the performance based on different metrics. Finally, LAMDA-RD is validated in a real case study related to the identification of production states in a gas-lift well, with data stream. The results have shown that LAMDA-RD achieves a competitive performance with respect to the other well-known algorithms, especially in unbalanced benchmarks and benchmarks with an overlapping of around 9%. In these cases, our algorithm is the best, reaching a Rand Index (RI) >98%. Besides, it is consistently among the best for all metrics considered (Silhouette coefficient, modification of the Silhouette coefficient, WB-index, Performance Coefficient, among others) in all case studies analyzed in this paper. Finally, in the real case study, it is better in all the metrics.
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Clustering algorithms, Partitioning algorithms, Production, Unsupervised learning, Data analysis, Proposals, Benchmark testing, Automatic merging, clustering, LAMDA, unsupervised learning