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Research and Applications of Mining Frequent Function Set from Datasets

Author: JiaXiaoBin
Tutor: TangChangJie
School: Sichuan University
Course: Computer Applications
Keywords: Data Mining Frequent Function Set (FFS) Constrained FFS GEP
CLC: TP311.13
Type: Master's thesis
Year: 2005
Downloads: 98
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Abstract


Data Mining is the hot topic of current database research and application. Function Mining is an important research direction of Data Mining to discover functions hidden in scientific databases. However, the traditional Function Mining is limited by the facts that (1) its purpose is to discover single function that lacks power to describe laws in the real world; and (2) it is difficult to be applied to complex databases. To break these limitations, the contributions of this article include: (1) Extending the concept of Function Mining and proposing a new mining object called Frequent Function Set (FFS) with powerful describing ability. FFS is referred to as function cluster on a specific dataset with support not less than the minimum support threshold. (2) Analyzing the property of FFS. (3) Presenting a new algorithm called Configurable Frequent Function Set Discovering Algorithm (CFFSDA) to mine FFS, which can flexibly be implemented by various searching algorithms. (4) By exploring the defects of CFFSDA, introducing Constrained FFS and presenting its mining framework. Constrained FFS can meet the need of various interests defined by users. (5) Applying Gene Expression Programming (GEP) to CFFSDA. Proposing a brand new strategy named Precision Threshold Queue (PTQ) in GEP to improve the probability of success. (6) Discussing the applications of FFS to database query-optimization and classification. Demonstrating that the query-optimization strategy using FFS is more efficient than traditional one when meeting the selection operation with WHERE Clause involving equality condition or some special comparison condition. (7) By extensive experiments, demonstrating the potentiality of FFS and its application to classification, also illustrating the action of PTQ which improves the success-probability by 55 times for mining complex functions with high precision. This article is organized as following: Chapter 0 talks about the importance and the significance of the study of Function Mining. Chapter 1 introduces Data Mining and the concept of Function Mining. Chapter 2 analyzes the limitations of traditional Function Mining, and then proposes Frequent Function Set and an algorithm called CFFSDA. By extending FFS, presents Constrained FFS and its mining framework. Chapter 3 briefly introduces GEP and Precision Threshold Queue, configures and implements CFFSDA. Chapter 4 illustrates the application of FFS to SQL query-optimization and classification. Chapter 5 gives experiments results. Chapter 6 concludes the paper with directions for future work.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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