A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method

Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patter...

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Bibliographic Details
Published inJournal of the American Society for Information Science and Technology Vol. 54; no. 7; pp. 660 - 670
Main Authors Chen, Ruey-Shun, Hu, Yi-Chung
Format Journal Article
LanguageEnglish
Published New York Wiley Subscription Services, Inc., A Wiley Company 01.05.2003
Wiley Periodicals Inc
Subjects
Data Analysis
Data mining
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Information retrieval
Information science
Intelligibility
Knowledge bases (artificial intelligence)
Knowledge representation
Natural language
Online information retrieval
Partition method
Partitions
Prototypes
Research subjects
Searching
Studies
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Summary:Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Moreover, each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. In this method, each attribute is partitioned by its various fuzzy sets with pre‐specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily obtained. The main aim of this paper is exactly to propose a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method.
Bibliography:Nomenclature K, number of partitions in each quantitative attribute; k, length of a fuzzy sequence; d, degree of a given relation, where d ≥ 1; A K,i mx m, im-th linguistic value of K fuzzy partitions defined in quantitative attribute xm, 1 ≤ im ≤ K; μ K,i mx m, membership function of A K,i mx m; n, total number of customers; cr, r-th customer, where 1 ≤ r ≤ n; αr, number of consecutive transactions ordered by transaction-time for cr; β, total number of frequent fuzzy grids; t p(r), p-th transaction corresponding to cr, where t p(r) = (t p1(r), t p2(r), ..., t pd(r)), and 1 ≤ p ≤ αr; Lj, j-th frequent fuzzy grid, where 1 ≤ j≤ β.
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ArticleID:ASI10258
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p1
fuzzy partitions defined in quantitative attribute
number of consecutive transactions ordered by transaction‐time for
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th frequent fuzzy grid, where 1
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membership function of
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Nomenclature
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degree of a given relation, where
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th transaction corresponding to
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number of partitions in each quantitative attribute
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length of a fuzzy sequence
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total number of customers
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ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1532-2882
2330-1635
1532-2890
2330-1643
DOI:10.1002/asi.10258