Feature Selection for Effective Calculation of a Similarity Measure

In any mining application for useful information from databases, an increasing number of features (attributes) makes worse results and loses much time. We propose a feature selection technique which saves computation time and does not spoil effect of mining. We take an algorithm called Iterated Contextual Distances (ICD) [1], show its problems for practical applications, and propose a feature selection method, which mitigates these problems. Then we show effects of the feature selection by experiments performed on a real dataset.