Detecting seasonal trends and cluster motion visualization for very high dimensional transactional data

Introduction Real life transactional data often poses challenges such as very large size, high dimensionality, skewed distribution, sparsity, seasonal variations and market-drift or migration [1, 2]. Most studies have taken a static view of the data while making predictions about a customer's buying behavior, market segmentation, etc. [3, 4]. A notable exception is recent work on temporal association rule mining, dealing with incremental characteristics and change, for example, see [5, 6]. This paper focusses on the problem of segmenting customers visiting a rapidly growing e-tailer. The segments are dynamic and seasonal, so preprocessing and trend characterization is key. We use a real-life data belonging to an e-commerce business and referred to as Horizon data in this paper, provided by KD1 1 (since then acquired by Net Perceptions) to illustrate the issues. In Section 2, the Horizon data is summarized. Section 3 quanties market migration