效用模式挖掘方法的研究

发布时间：2024-02-26 01:04

　　互联网、物联网、云计算等信息技术的快速发展,政治、经济、军事、工业等各个领域的传统应用开始与之相结合,产生了比以往任何时候都要多的数据。同时,智能移动设备、传感器、电子商务网站、社交网站等等数据来源每时每刻都在创造多种多样的数据。面对如此大量的数据,如何及时、有效地分析它们并从中提取出有价值的信息,是政府和企业亟待解决的问题。例如,中国证券监督管理委员会(CSRC)通过股票买家和卖家的交易价格和数量来判断是否存在交易内幕和炒家的操控;支付宝网络科技公司通过分析支付宝用户在网络平台上的消费记录获取不同用户的消费习惯并制定相应的市场策略;交通部以不同的时间间隔分析道路网络的交通流量信息,并制定减少城市交通拥堵的政策。数据挖掘作为一种从大量数据中挖掘重要的、未知的、有潜在价值的模式的处理过程,被广泛用于解决这类问题。关联规则挖掘(ARM)是数据挖掘的核心任务之一。然而,依赖支持度(value of support)提取模式的传统方法并不能很好支持依据效用(value of utility)的模式提取。因此,效用模式挖掘,这是我们研究的主题,已经出现了为了满足这一需求。最近,在这一领域提出了许...

【文章页数】：140 页

【学位级别】：博士

【文章目录】：
摘要
Abstract
Notation
Chapter 1 Introduction
    1.1 Background
    1.2 Motivations
    1.3 Contributions
    1.4 The Structure of Thesis
Chapter 2 Related Work and Preliminaries
    2.1 Related Work
        2.1.1 High Utility Itemset Mining Algorithms on static Database
        2.1.2 Top-k High Utility Itemset Mining on Static Database
        2.1.3 Closed High Utility litemset Mining on Static Database
        2.1.4 High Utility Itemset on Incremental Mining
        2.1.5 High Utility Itemset Mining on Data Streams
        2.1.6 Multiple Minimum Utility Threshold Methods
        2.1.7 Using Support and Utility Thresholds
        2.1.8 Variants Problems
    2.2 Preliminaries
        2.2.1 Definitions
Chapter 3 SSUP-Growth:A Novel Mining High Utility Itemset Algorithm with Single-Scan of Database
    3.1 Introduction
    3.2 Problem Statement
    3.3 Proposed Approach
        3.3.1 The Components of SSUP-Tree
        3.3.2 Building the SSUP-Tree
    3.4 Achieving UP-tree From SSUP-tree
        3.4.1 Update SSUP-tree with New Data
    3.5 Experimental Evaluation
        3.5.1 Settings
        3.5.2 Evaluation on Synthetic and Real Datasets
        3.5.3 Scalability
    3.6 Conclusion
Chapter 4 Mining High utility Itemset with An Improved MultipleMinimum Utility Based Approach
    4.1 Introduction
        4.1.1 Different From Previous Works
        4.1.2 Problem Statement
    4.2    The Proposed Approach
        4.2.1 Preliminaries
        4.2.2 The basic Idea
    4.3 HUI-MMU-UD Algorithm
    4.4 Experimental Result
        4.4.1 Pattern Analysis
        4.4.2 Runtime
        4.4.3 Memory Usage
    4.5 Conclusion
Chapter 5 LUIM: New Low Utility Itemset Mining Framework
    5.1 Introduction
        5.1.1 Motivation
    5.2 Problem statement
    5.3    Proposed Framework
        5.3.1 Low Utility Itemset Mining Framework
        5.3.2 Low Utility Generators Miner Algorithms
        5.3.3 Low Utility Itemset Mining Algorithm
    5.4 Performance evaluation
        5.4.1 Experimental Environment and Datasets
        5.4.2 Runtime
        5.4.3 Memory Usage
        5.4.4 Generated Items Comparison
        5.4.5 Discussion
    5.5 Conclusion
Chapter 6 FLUI-Growth:Frequent Low-Utility Itemsets Mining
    6.1 Introduction
    6.2 Problem Formulation
    6.3 The Proposed Method
        6.3.1 The components of FLUP-Tree
        6.3.2 Building LUP-Tree
        6.3.3 FLUI-Growth
    6.4 Experimental Result
    6.5 Conclusion
Chapter 7 Conclusion and future work
    7.1 Conclusion
    7.2 Future work
Bibliography
Acknowledgements
Publications



本文编号：3911104