Clustering of large time-series datasets using a multi-step approach / Saeed Reza Aghabozorgi Sahaf Yazdi

TL;DRAbstract

Various data mining approaches are currently being used to analyse data within different domains. Among all these approaches, clustering is one of the most-used approaches, which is typically adopted in order to group data based on their similarities. The data in various systems such as finance, healthcare, and business, are stored as time-series. Clustering such complex data can discover patterns which have valuable information. Time-series clustering is not only useful as an exploratory technique but also as a subroutine in more complex data mining algorithms. As a result, time-series clustering (as a part of temporal data mining research) has attracted increasing interest for use in various areas such as medicine, biology, finance, economics, and in the Web. Several studies which focus on time-series clustering have been conducted in said areas. Many of these studies focus on the time complexity of time-series clustering in large datasets and utilize dimensionality reduction approac

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