Abstract
Clustering is a well established data exploration and analysis method. It allows interactive discovery and interpretation of groups of entities that have similar properties and characteristics. However, deriving meaningful insights from clusters often presents challenges in large sets of structurally complex data. Large scale commercial enterprises hold an increasing volume of complex, highly-dimensional data. In order to effectively analyze this data and create meaningful clusters from it, pre-processing the data prior to clustering is essential. Once clusters are created, interpretation and representation of clusters is equally essential to capture insights that can aid corporate decision making. In this paper, we present a generic approach to data preparation and cluster interpretation implemented on a large scale enterprise database.
Published Version
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