![](/static/media/logo.jpg)
An International Publisher for Academic and Scientific Journals
Author Login
Scholars Journal of Engineering and Technology | Volume-5 | Issue-07
Time Series Based Clustering Using K-Means and Hierarchical Techniques for Efficient Mining of Sales Data
Nidhi Tiwari, Toran Verma
Published: July 30, 2017 |
132
87
DOI: 10.21276/sjet
Pages: 355-358
Downloads
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.