Higherrarchical Data Mining for Unusual Sub-sequence Identifications in Time Series Processes

Analytical capability limitations and strict modelling assumptions have hindered progress and the amount of information extracted from large datasets remained limited for a long time. The advent of computers and fast operating systems coupled with user-friendly platforms have changed that and made a grate impact on data mining and analysis that have been problematic both from theoretical and practical ways. In this paper we introduce and investigate a hierarchical and efficient approach for mining time series data to identify block discords. Each identified block discord is further mined fordiscord sub-sequences within. In this and as mentioned in [1], we aim to publicise the use of classical statistics and Time Series tools as efficient data mining tools. We examine these tools on real data. The technique will further be applied on the identified group discord to search for sub-sequence discord.