A Short Introduction to Sequential Data Mining
Koji IWANUMA Hidetomo NABESHIMA
University of Yamanashi
The First Franco-Japanese Symposium on Knowledge Discovery in System Biology, September 17, Aix-en-Provence
Two Main Frameworks of Sequential Mining
Sequential pattern mining for multiple data sequences
Sequence ID 1 2 3 4 5 Purchase data record
<(wheat, milk), bread, (berry, sausage)> <(bread, pumpkin, sausage)>
Sequential pattern mining for a single data sequence
Data sequence
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J. Han and M. Kamber. Data Mining: Concepts and Techniques, www.cs.uiuc.edu/~hanji
What Is Sequential Pattern Mining?
Given a set of sequences, find the complete set of frequent subsequences
A
sequence : < (ef) (ab)
(df) c b >
A sequence database
SID 10 20 30 sequence <(ad)c(bc)(ae)> <(ef)(ab)(df)cb> An element may contain a set of items. Items within an element are unordered and we list them alphabetically.
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is a subsequence of
Given support threshold min_sup =2, <(ab)c> is a
sequential pattern
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Challenges on Sequential Pattern Mining
A huge number of possible sequential patterns are hidden in databases
A mining algorithm should find the complete set of patterns, when possible, satisfying the minimum support (frequency) threshold be highly efficient, scalable, involving only a small number of database scans
be able to incorporate various kinds of user-specific constraints
J. Han and M. Kamber. Data Mining: Concepts and Techniques, www.cs.uiuc.edu/~hanji
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Sequential Pattern Mining Algorithms for Multiple Data Sequences
Apriori-based method: GSP (Generalized Sequential Patterns: Srikant & Agrawal @ EDBT’96) Pattern-growth methods: FreeSpan & PrefixSpan (Han et al.@KDD’00; Pei, et al.@ICDE’01) Vertical format-based mining: SPADE (Zaki@Machine Leanining’00) Constraint-based sequential pattern mining (SPIRIT: Garofalakis,
Rastogi, Shim@VLDB’99; Pei, Han, Wang @ CIKM’02)
Mining closed sequential patterns: CloSpan (Yan, Han & Afshar @SDM’03)
J. Han and M. Kamber. Data Mining: Concepts and Techniques, www.cs.uiuc.edu/~hanji
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Mining Sequential Patterns from a Very-Long Single Sequence
A series of daily news paper articles
<
typhoon flood, landslide typhoon flood, landslide
>
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Sequential Pattern Mining Algorithms for a Single data Sequence
Discovery of frequent episodes in event sequences, based on a sliding window system [Mannila 1998]：
The frequency measure becomes anti-monotonic, but has a problem, i.e., a duplicate counting of an occurrence.
Asynchronous periodic pattern mining [Yang et.al 2000, Huang 2004]：
Any anti-monotonic frequency measures are not investigated.
On-line approximation algorithm for mining frequent items, not for frequent subsequences
Lossy counting algorithm [Manku and Motwani, VLDB’02]
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Research in Our Laboratory
Sequential Data Mining from a very-large single data sequence.
Main target: sequential textual data, especially, newspaper-articles corpora Objectives: to generate a robust and useful largescale event-sequences corpus.
Application 1： topic tracking/detection in information retrieval. Application 2： automated content-tracking in WEB. Application 3: scenario/story semi-automatic creation
Ordinary temporal data analysis: various log data in computer systems, genetic information, etc.
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Technical Topics (1/2)
A new framework for extracting frequent subsequences from a single long data sequence:
in IEEE Inter. Conf. on Data Mining 2005 (ICDM2005):
A new rational frequency measures, which satisfies the Apriori (anti-monotonic) property and has no duplicate counting. A fast on-line algorithm for a some limited case
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Technical Topics (1/2)
On-going current works and future work
On-line rational filters based on confidence criteria and/or information-gain for eliminating redundant valueless sequences from system output Methods for finding meta-structures embedded in huge amount of frequent sequences generated by a system
A method using compression based on context-free grammarinference/learning
More fast extraction algorithm based on a method for simultaneously searching multiple strings over compressed data.
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References:
Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques (Chapter 8). www.cs.uiuc.edu/~hanj
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Thanks for your attention!!
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