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Increasing Efficiency of Time Series Clustering by Dimension Reduction Techniques


Saeid Bahadori and Nasrollah Moghadam Charkari


Vol. 18  No. 5  pp. 164-170


Finding similar time series has attracted a lot of interest and much research has been done recently as a result [1]. For the reason of high dimension of the time series data, finding a good answer to this problem is difficult. Encounter with these high dimensional data requires us to use dimension reduction techniques, and then performing data mining tasks on reduced dataset. Several time series dimension reduction techniques have been proposed before, such as DFT [2], DWT [3], SVD [4], PAA [5] and [31], APCA [6], PLA [7], SAX [8] and many others, but we cannot simply choose an arbitrary compression algorithm [9]. Each one of these algorithms has different answers to a unique problem. The main contribution of this paper reviewing the time series data mining and data mining literature. In this research we have been compared result of clustering after reducing dimension of data set with two different well-known algorithms, DFT and DWT techniques. Finally, we proposed energy ratio algorithm to find out the most efficient number of dimensions in a new space.


Time series, Data mining, Dimension reduction, Cluster validity measures.