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文件名称：数据挖掘经典论文.rar
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经典论文 数据挖掘 必看
数据挖掘方向的五十篇经典论文，数据挖掘学习必看论文。
【文件预览】：
数据挖掘经典论文
----【LM1】pagerank.pdf(300KB)
----【CL1】Clarans.pdf(279KB)
----【CL6-STING】[VLDB 1997] STING A Statistical Information Grid Approach to Spatial Data Mining.PDF(1003KB)
----【CL5-OPTICS】[SIGMOD 1998] OPTICS Ordering Points To Identify the Clustering Structure.pdf(251KB)
----[DMKD 2004]FP-growth.pdf(345KB)
----【AFP2】[SDM 2002] CHARM An Efficient Algorithm for Closed Itemset Mining.pdf(534KB)
----【AFP3-CLOSET】[DMKD 2000] CLOSET An Efficient Algorithm for Mining Frequent Closed Itemsets.pdf(252KB)
----【SP2】[EDBT 1996]GSP.pdf(187KB)
----【LM2】google.pdf(121KB)
----【SP6】[KDD 2004] IncSpan.pdf(147KB)
----【CL6】[VLDB 1997] STING.PDF(1003KB)
----【SP3】[ML 2001] SPADE An Efficient Algorithm for Mining Frequent Sequences.pdf(583KB)
----【CL2】[KDD 1996] DBSCAN.pdf(103KB)
----【CL7-CobWeb】[Machine Learning, 1987] Knowledge Acquisition Via Incremental Concept Learning.pdf(3.46MB)
----【CL3-BIRCH】[SIGMOD 1996] BIRCH A Efficient Data Clustering Method for Very Large Databases.pdf(1.39MB)
----[ICDT 1999] Discovering frequent closed itemsets for association rules.pdf(815KB)
----【FP3-FPgrowth】[SIGMOD 2000] Mining Frequent Patterns without Candidate Generation.pdf(265KB)
----【AFP3】[DMKD 2000] CLOSET An Efficient Algorithm for Mining Frequent Closed Itemsets.pdf(252KB)
----【SP2-GSP】[EDBT 1996]GSP Mining Sequential Patterns Generalizations and Performance Improvements.pdf(187KB)
----【AFP1】[SIGMOD 1998] Efficiently mining long patterns from databases.pdf(179KB)
----【SP4-PrefixSpan】[ICDE 2001] PrefixSpan Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth.pdf(170KB)
----【SP5】[SDM 2003] CloSpan.pdf(383KB)
----【FP1】[SIGMOD 1993] Mining Association Rules between Sets of Items in Large Databases.pdf(210KB)
----【FP2-Aprior】[VLDB 1994] Fast Algorithms For Mining Association Rules.pdf(266KB)
----【CL5】[SIGMOD 1998] OPTICS.pdf(251KB)
----【LM3-HITS】Authoritative Sources in a Hyperlinked Environment.pdf(191KB)
----【SP3-SPADE】[ML 2001] SPADE An Efficient Algorithm for Mining Frequent Sequences.pdf(583KB)
----[KDD 2002] Efficiently Mining Frequent Trees in a Forest.pdf(1.19MB)
----【CL1-Clarans】Efficient and Effective Clustering Method for Spatial Data Mining.pdf(279KB)
----【GP2-CloseGraph】[KDD 2003] CloseGraph Mining Closed Frequent Graph Patterns.pdf(362KB)
----【CL4-CURE】[SIGMOD 1998] CURE An Efficient Clustering Algorithm for Large Databases.pdf(1.63MB)
----【AFP1-MaxMiner】[SIGMOD 1998] Efficiently mining long patterns from databases.pdf(179KB)
----chameleon hierarchical clustering.pdf(1.58MB)
----【GP2】[KDD 2003] CloseGraph.pdf(362KB)
----【CL4】[SIGMOD 1998] CURE.pdf(1.63MB)
----【CL3】[SIGMOD 1996] BIRCH.pdf(1.39MB)
----【LM3】HITS.pdf(191KB)
----【AFP4-CLOSET+】[SIGKDD 2003] CLOSET+ Searching_for_the_Best_Strategies_for_Mining_Frequent_Closed_Itemsets.pdf(431KB)
----【SP1】[ICDE 1995] Mining Sequential Pattern.pdf(242KB)
----【AFP2-CHARM】[SDM 2002] CHARM An Efficient Algorithm for Closed Itemset Mining.pdf(534KB)
----【SP6-IncSpan】[KDD 2004] IncSpan Incremental Mining of Sequential Patterns in Large Datasets.pdf(147KB)
----【FP2】[VLDB 1994] Fast Algorithms For Mining Association Rules.pdf(266KB)
----【GP1】[ICDM 2002] gSpan.pdf(245KB)
----【GP1-gSpan】[ICDM 2002] gSpan Graph-Based Substructure Pattern Mining.pdf(245KB)
----[TKDE 2005] Efficiently Mining Frequent Trees in a Forest Algorithms and Applications.pdf(1.38MB)
----【FP3】[SIGMOD 2000] Mining Frequent Patterns without Candidate Generation.pdf(265KB)
----【SP4】[ICDE 2001] PrefixSpan.pdf(170KB)
----【LM2-Google】The Anatomy of a Large-Scale Hypertextual Search Engine.pdf(121KB)
----【CL2-DBSCAN】[KDD 1996] A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.pdf(103KB)
----【AFP4】[SIGKDD 2003] CLOSET+ Searching_for_the_Best_Strategies_for_Mining_Frequent_Closed_Itemsets.pdf(431KB)
----【SP5-CloSpan】[SDM 2003] CloSpan Mning Closed Sequential Patterns in Large Datasets.pdf(383KB)
----【LM1-PageRank】The PageRank Citation Ranking Bringing Order to the Web.pdf(300KB)
----【CL7】[Machine Learning, 1987] CobWeb.pdf(3.46MB)