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» Incremental Mining of Sequential Patterns in Large Databases
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ICDE
2007
IEEE
104views Database» more  ICDE 2007»
14 years 1 months ago
Filtering Frequent Spatial Patterns with Qualitative Spatial Reasoning
In frequent geographic pattern mining a large amount of patterns can be non-novel and non-interesting. This problem has been addressed recently, and background knowledge is used t...
Vania Bogorny, Bart Moelans, Luis Otávio Al...
JCP
2006
173views more  JCP 2006»
13 years 7 months ago
Database Intrusion Detection using Weighted Sequence Mining
Data mining is widely used to identify interesting, potentially useful and understandable patterns from a large data repository. With many organizations focusing on webbased on-lin...
Abhinav Srivastava, Shamik Sural, Arun K. Majumdar
AICCSA
2008
IEEE
292views Hardware» more  AICCSA 2008»
14 years 2 months ago
Enumeration of maximal clique for mining spatial co-location patterns
This paper presents a systematic approach to mine colocation patterns in Sloan Digital Sky Survey (SDSS) data. SDSS Data Release 5 (DR5) contains 3.6 TB of data. Availability of s...
Ghazi Al-Naymat
KDD
2008
ACM
165views Data Mining» more  KDD 2008»
14 years 8 months ago
Colibri: fast mining of large static and dynamic graphs
Low-rank approximations of the adjacency matrix of a graph are essential in finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track ...
Hanghang Tong, Spiros Papadimitriou, Jimeng Sun, P...
KDD
2009
ACM
188views Data Mining» more  KDD 2009»
14 years 8 months ago
Mining discrete patterns via binary matrix factorization
Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rankone binary matrix approximations that identify the dominant patt...
Bao-Hong Shen, Shuiwang Ji, Jieping Ye