Time Series Based Clustering Using K-Means and Hierarchical Techniques for Efficient Mining of Sales Data
Journal Title: Scholars Journal of Engineering and Technology - Year 2017, Vol 5, Issue 7
Abstract
Data mining is the combination of data assembled by customary information mining philosophies and procedures with data accumulated over large no of data sources. Time-Series clustering is one of the important concepts of data mining that is used to gain insight into the mechanism that generate the time-series and predicting the future values of the given time-series. Time series clustering is a task that aims to assign each of the time series a class label from two or more classes having some training data. Classification is needed to distinguish between different types of time series. K-Means is one of the common and simplest unsupervised algorithms for clustering. It classifies data on the basis of Euclidian distance. In data mining and statistics, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Time Series based clustering is used to classify time series data by K-Means or Hierarchical clustering. The proposed work aims to extract important information from Time Series sales data for analysing current sale trends for efficient Market Basket Analysis. The work also aims to provide better analysis of the proposed approach on the basis of obtained numerical results. Keywords: Data Mining, K-Means Algorithm, Hierarchical Clustering, Market Basket Analysis.
Authors and Affiliations
Nidhi Tiwari, Toran Verma
Keywords
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