  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.
