Clustering Time Series Data Stream - A Literature Survey

TL;DR

This survey reviews existing clustering algorithms for time series data streams, highlighting their challenges, applications, and limitations, and discusses future research directions in this evolving field.

Contribution

It provides a comprehensive overview of current clustering methods for time series streams, analyzing their effectiveness and identifying gaps for future research.

Findings

Various clustering algorithms have been summarized and compared.

Challenges include handling large data and outliers.

Future research topics are identified.

Abstract

Mining Time Series data has a tremendous growth of interest in today's world. To provide an indication various implementations are studied and summarized to identify the different problems in existing applications. Clustering time series is a trouble that has applications in an extensive assortment of fields and has recently attracted a large amount of research. Time series data are frequently large and may contain outliers. In addition, time series are a special type of data set where elements have a temporal ordering. Therefore clustering of such data stream is an important issue in the data mining process. Numerous techniques and clustering algorithms have been proposed earlier to assist clustering of time series data streams. The clustering algorithms and its effectiveness on various applications are compared to develop a new method to solve the existing problem. This paper presents a survey on various clustering algorithms available for time series datasets. Moreover, the distinctiveness and restriction of previous research are discussed and several achievable topics for future study are recognized. Furthermore the areas that utilize time series clustering are also summarized.