Optimization-Assisting Dual-Step Clustering of Time Series Data

Abstract

This paper aims to propose a new time series data clustering with the following steps: (1) data reduction and (2) clustering. The main objective of the time series data clustering is to minimize the dataset size via a prototype defined for same time series data in every group that significantly reduced the complexities. Initially, the time series dataset in the data reduction step is subjected to preprocessing process. Further, in the proposed probability based distance measure evaluation, the time series data is grouped into subclusters. In the clustering step, the proposed shape based similarity measure is performed. Moreover, the clustering process is carried out by optimized k-mean clustering in which the center point is optimally tuned by a new customized whale optimization algorithm (CWOA). At last, the performance of the adopted model is computed to other traditional models with respect to various measures such as sensitivity, accuracy, FPR, conentropy, precision, FNR, specificity, MCC, entropy, F-measure, and Rand index, respectively.