Mining the stock market (extended abstract)
Martin Gavrilov, Dragomir Anguelov, Piotr Indyk, Rajeev Motwani · 2000
In recent years, there has been a lot of interest in the database community in mining time series data.Surprisingly, little work has been done on verifying which measures are most suitable for mining of a given class of data sets.Such work is of crucial importance, since it enables us to identify similarity measures which are useful in a given context and therefore for which efficient algorithms should be further investigated.Moreover, an accurate evaluation of the performance of even existing algorithms is not possible without a good understanding of the data sets occurring in practice.In this work we attempt to fill this gap by studying similarity measures for clustering of similar stocks (which, of course, is an interesting problem on its own).Our approach is to cluster the stocks according to various measures (including several novel ones) and compare the results to the "groundtruth" clustering based on the Standard and Poor 500 Index.Our experiments reveal several interesting facts about the similarity measures used for stock-market data.