Identifikasi Motif Time Series Data Mining dengan Euclid

Abstract

The development of information technology that is supported by storage media technology has brought about major changes to the availability of data warehouses. The availability of data warehouses is often overlooked because of the inability to process the data so that data stacks are often regarded as garbage, which should be used as decision support, one form of data warehouse that is often encountered today is time series. Therefore it is necessary to develop a method to improve the motive discovery of time series data mining. The method used is discretion so that it produces time series sub sequences, which are then clustered. The distance used is the euclid distance. The results obtained are the finding of motives in time series based on time that can be used as decision support and predictions in the future