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Mining frequent patterns without candidate generation
This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
- J. Pei, Jiawei Han, M. Hsu
- Computer ScienceProceedings 17th International Conference on Data…
- 2 April 2001
This work proposes a novel sequential pattern mining method, called Prefixspan (i.e., Prefix-projected - Ettern_ mining), which explores prejxprojection in sequential pattern Mining, and shows that Pre fixspan outperforms both the Apriori-based GSP algorithm and another recently proposed method; Frees pan, in mining large sequence data bases.
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
- Jiawei Han, J. Pei, Yiwen Yin, Runying Mao
- Computer ScienceSixth IEEE International Conference on Data…
A novel frequent-pattern tree (FP-tree) structure is proposed, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and an efficient FP-tree-based mining method, FP-growth, is developed for mining the complete set of frequent patterns by pattern fragment growth.
Data Mining: Concepts and Techniques, 3rd edition
There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
CMAR: accurate and efficient classification based on multiple class-association rules
- Wenmin Li, Jiawei Han, J. Pei
- Computer ScienceProceedings IEEE International Conference on…
- 29 November 2001
The authors propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules, which extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large databases efficiently.
Mining sequential patterns by pattern-growth: the PrefixSpan approach
This paper proposes a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns, and shows that PrefixSpan outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE and is the fastest among all the tested algorithms.
Asymmetric Transitivity Preserving Graph Embedding
A novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), is developed, which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
Preserving Privacy in Social Networks Against Neighborhood Attacks
The empirical study indicates that anonymized social networks generated by the method can still be used to answer aggregate network queries with high accuracy and present a practical solution to battle neighborhood attacks.
FreeSpan: frequent pattern-projected sequential pattern mining
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Community Preserving Network Embedding
A novel Modularized Nonnegative Matrix Factorization (M-NMF) model is proposed to incorporate the community structure into network embedding and jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures.