Temporal Correlation in Sequential Pattern Analysis

Dr. Chuanren Liu
Drexel University


Abstract

Sequential pattern analysis aims at finding statistically relevant temporal structures where the values are delivered in sequences. This is a fundamental problem in data mining with diversified applications in many science and business fields. Given the overwhelming scale and the dynamic nature of the sequential data, new visions and strategies for sequential pattern analysis are required to derive competitive advantages and unlock the power of the big data. To this end, in this talk, we present novel approaches for sequential pattern analysis using temporal correlation. Particularly, we will focus on the “temporal skeletonization”, our approach to identifying the meaningful granularity for sequential pattern mining. We first show that a large number of symbols in a sequence can “dilute” useful patterns which themselves exist at a different level of granularity. This is so-called “curse of cardinality”, which can impose significant challenges to the design of sequential analysis methods. To address this challenge, our key idea is to summarize the temporal correlations in an undirected graph, and use the “skeleton” of the graph as a higher granularity on which hidden temporal patterns are more likely to be identified. In the meantime, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data.