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Ming Frequent Free Tree Based on Sequence Pattern

Author: SunShengJun
Tutor: GuoPing
School: Chongqing University
Course: Applied Computer Technology
Keywords: Data Mining Freedom Tree Frequent subtrees Frequent sequences TDB
CLC: TP301.6
Type: Master's thesis
Year: 2008
Downloads: 59
Quote: 0
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Abstract


Data Mining (Data Mining, DM) is from large database or data warehouse to extract implicit, unknown, non-trivial and potentially valuable information, or mode. Since the concept was put forward in data mining ten years, data mining technology has been increasing attention and wide range of applications and research. Therefore, as an important branch of data mining frequent itemsets and association rule mining, is attracting a lot of attention and get a larger research and development. With data mining applications continues to expand and the kind of data related to the increase, especially in the development of network technology, the traditional areas for structured relational database and transaction database mining technology can not meet the non-traditional areas of data mining technology requirements, such as: Semi-structured data types and unstructured data types. These data types in bioinformatics, Web mining, compound structure analysis and other fields have a wide range of applications. In this paper, for unstructured data - trees and acyclic graph mining techniques conducted in-depth research and analysis. Its main tasks are: First, the data mining technology background knowledge in-depth description and analysis. Which focuses on data mining technology is an important branch - association rule mining. Summary of different types of mining association rules, and one of the frequent itemset mining to do a comprehensive in-depth introduction. Secondly, for the main tree structure mining algorithms were categorized and compare the efficiency of two types of algorithms, concluding a depth-first algorithm efficiency. Such as the direction of this study identify the entry point at the back of the algorithm is used for the depth-first, vertical search approach. Then, the analysis of the current depth-first algorithm more efficient two classical algorithms, TreeMiner and FreeTreeMiner, summarize and analyze their strengths and weaknesses, and to follow-up algorithms used for the author. Then, facing acyclic graph (free tree) type of algorithm of planning, is divided into four steps: (1) the center of the tree in search of freedom, which, the authors propose efficient LWA (Longest Way Algorithm) algorithm, and prove the correctness of the algorithm and efficiency. (2) on a rooted unordered tree for standardization, the authors propose here canonicalization algorithm Canonicalization, and analyze the time complexity of this algorithm is to prove that the time complexity of the current most efficient algorithm is quite similar. (3) mining frequent sequential patterns, the author of the \(4) The method of introducing an index different sequences having the same tap structure frequent subtrees. Finally, the experimental comparison algorithm SFTM (SequenceFreeTreeMiner) and similar Chopper algorithm, FreeTreeMiner algorithm to verify SFTM algorithm efficiency and correctness.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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