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Sequential Pattern Analysis with the Right Granularity
Chuanren Liu
Ph.D Candidate in Information Systems
Rutgers Business School
February 16, 2015
9:30-10:20 am
W181 Pappajohn Business Building
Abstract
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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 with applications in
dynamic business environments. 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. Evaluation on a B2B (Business to Business) marketing application
demonstrates that our approach can effectively discover critical buying paths from noisy
customer event data.