1. 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

2. Two Main Frameworks of Sequential Mining
Sequential pattern mining for multiple data sequences
Sequential pattern mining for a single data sequence
Sequence ID
Purchase data record 1
<bread, cheese> 2
<(wheat, milk), bread, (berry, sausage)> 3
<(bread, pumpkin, sausage)> 4
<bread, cheese, sausage> 5
<cheese>
Data sequence
<S1 S2 S3 S4 S5 S6 S7 … … Sn>

3. What Is Sequential Pattern Mining?
J. Han and M. Kamber. Data Mining: Concepts and Techniques,
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
sequence 10
<a(abc)(ac)d(cf)> 20
<(ad)c(bc)(ae)> 30
<(ef)(ab)(df)cb> 40
<eg(af)cbc>
An element may contain a set of items.
Items within an element are unordered
and we list them alphabetically.
<a(bc)dc> is a subsequence of <a(abc)(ac)d(cf)>
Given support threshold min_sup =2, <(ab)c> is a sequential pattern

4. 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,

5. Sequential Pattern Mining Algorithms for Multiple Data Sequences
Apriori-based method: GSP (Generalized Sequential Patterns: Srikant & EDBT’96)
Pattern-growth methods: FreeSpan & PrefixSpan (Han et Pei, et
Vertical format-based mining: SPADE Leanining’00)
Constraint-based sequential pattern mining (SPIRIT: Garofalakis, Rastogi, Pei, Han, CIKM’02)
Mining closed sequential patterns: CloSpan (Yan, Han &
J. Han and M. Kamber. Data Mining: Concepts and Techniques,

6. Mining Sequential Patterns from a Very-Long Single Sequence
A series of daily news paper articles
<
>
typhoon
flood,
landslide
typhoon
flood,
landslide
<typhoon (flood, landslide)>

7. 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]

8. 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 large-scale 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.

9. 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

10. 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 grammar-inference/learning
More fast extraction algorithm based on a method for simultaneously searching multiple strings over compressed data.

11. References:
Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques (Chapter 8).

12. Thanks for your attention!!