Temporal outlier detection on quantitative data using unexpectedness measure

Most of the mining techniques has only concerned with interesting patterns. However, in the recent years, there is an increasing demand of mining the Unexpected Items or Outliers or Rare Items. Several application domains have realized the direct mapping between outliers in data and real world anomalies that are of great interest to an analyst. Outliers represents semantically correct but infrequent situation in a database. Detecting outliers allows extracting useful and actionable knowledge to the domain experts. Our method which is used to extract temporal outliers is based on association rules, to infer the normal behavior of objects by extracting frequent rules from Stock Market dataset which is of quantitative, multidimensional and time series in nature. Then the temporal association rules are pruned by the unexpectedness measure - Residual Leverage to generate Temporal Outliers. The temporal outliers from Stock Market database are the Stock Split that occurs on a particular day. A stock split results in a stock price increase following the decrease immediately. This split provides a signal to the market which in turn increases the demand and prices.