Mining patterns in complex data

Abstract

Over the last decade, there has been an enormous growth in both the amount and the complexity of records that is collected and processed by humans and machines. This rapid growth has spurred interest in complex records that involve multiple kinds of data. Many applications from the clinical, surveillance and bioinformatics domains are now generating records withmultiple kinds of data. For these applications to reach their full potential, we need to build effective techniques to analyze such complex records. Frequent pattern mining, a data mining technique, is widely used in data analysis and decision support. However, previous work has focused primarily on mining patterns from categorical data, numerical data, and sequence data. Very little attention has been paid to mine patterns from interval data, time series data and datasets with multiple kinds of data. In this work, we seek to develop techniques for analyzing complex records where each record is a combination of categorical, numerical, interval and time series data. Specifically, we address the following questions pertaining to mining patterns from complex records: How can we find frequent patterns from interval data? How can we discover frequent patterns from time series data? How can we mine frequent patterns from complex records having multiple kinds of data?