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A Research on Algorithms of Mining Changes over Data Stream

Author: WangXiaoLong
Tutor: MaRuiMin
School: Daqing Petroleum Institute
Course: Applied Computer Technology
Keywords: Data Stream Mining Algorithm Summary data structures Change Entropy Sampling Minimum Description Length
CLC: TP311.13
Type: Master's thesis
Year: 2006
Downloads: 277
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Abstract


The changes on the data stream mining algorithm is one of the core content of the recent data flow field. The first portion of the text mining algorithm: Most of the existing research work for the change of the mode classification explore, the higher concept hierarchy of the method and the results obtained. In this paper, the method of mining \reduction, with reference to the tuples in the window specified by the user or expert and updated, and then using the town is calculated separately for each tuple in the current window with reference to the window between the specific tuple dissimilarity, and is described based on the results obtained , finally, the use of a plurality of interval monitoring the change in trend of the \Analysis to know the method sensitivity and real-time, and the mining results as well as the trends described in the form of more concise. Part II: The text mining algorithm research proposed the NBCC algorithm, first using accurate sampling method to build a data flow summary data structure, and then draw on the thinking of the classic Naive Bayes classification training sample set of the data stream is divided into Ci class , i = 1, 2, ..., m, the sample set of the data flow testing set a threshold value α, when P (x | Ci) * P (Ci) LT; alpha, i.e. when the test sample X belong to any known class Ci is less than the probability of the set of α, it indicates that a change has occurred, and to keep the change in the data stream, referred to as a new class Cm is 1. The repeated use of the change in the method of mining on data streams. The third part of the text mining algorithm research: The focus of the study is to support frequent itemsets and association rules novel continuous mining data stream changes. The main contribution is: (1) Construction of the summary data structure obtained by sampling the stream of data in units of tuples, the method can further reduce the size of the problem solving; (2) by calculating and comparing the data stream on the current window and reference the frequent mode support window and association rules freshness metrics and continuous mining data stream changes. The analysis shows that the continuous data stream mining method proposed is reasonable and feasible. The fourth part of the text mining method: preliminary study on the application of the minimum description length principle in the data stream.

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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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