Concept Hierarchy-Based Pattern Discovery in Time Series Database: A Case Study on Financial Database

Abstract

Data Mining is the process of automatically searching large volumes of data for patterns and it is also a fairly recent and contemporary topic in computing. Nowadays, pattern discovery is a field within the area of data mining. In general, large volumes of time series data are contained in financial database and these data have some useful but not easy finding patterns in it and many financial studies in time series data analysis use linear regression model to estimate the variation and trend of the data. However, traditional methods of time series analysis used special types or linear models to describe the data. Linear models can achieve high accuracy when linear variation of the data is small, however, if the variation range exceeds a certain limit, the linear models has a lower performance in estimated accuracy. SOM is a famous non-linear model and traditional method to extract pattern with numeric data. Many researches extract pattern from numeric data attributes rather than categorical or mixed data. It does not extract the major values from pattern rules, either. The purpose of this study is to provide a novel architecture in mining patterns from mixed data that uses a systematic approach in the financial database information mining, and try to find the patterns for estimate the trend or for special event's occurrence. This study uses ESA algorithm to discover the pattern in the Concept Hierarchy based Pattern Discovery (CHPD) architecture. Specifically, this architecture facilitates the direct handling of mixed data, including categorical and numeric values. This mining architecture can simulate human intelligence and discover patterns automatically, and it also demonstrates knowledge pattern discovery and rule extraction.