Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Also data mining is known as one of the core processes of Knowledge
|
2961
|
Pattern Classification and Scene Analysis
– Duda, Hart
- 1973
|
|
1607
|
Fast Algorithms for Mining Association Rules
– Agrawal, Srikant
- 1994
|
|
1449
|
Mining association rules between sets of items in large databases
– Agrawal, Imielinski, et al.
- 1993
|
|
735
|
Data Mining: Concepts and Techniques
– Han, Kamber
- 2000
|
|
659
|
Mining sequential patterns
– Agrawal, Srikant
- 1995
|
|
537
|
Mining frequent patterns without candidate generation
– Han, Pei, et al.
- 2000
|
|
342
|
Dynamic itemset counting and implication rules for market basket data
– Brin, Motwani, et al.
- 1997
|
|
299
|
Mining sequential patterns: Generalizations and performance improvements
– Srikant, Agrawal
- 1996
|
|
297
|
Discovery of Multiple-Level Association rules from Large Databases
– HAN, FU
- 1995
|
|
285
|
An Efficient Algorithm for Mining Association Rules in Large Databases
– Savasere, Omiecinski, et al.
- 1995
|
|
269
|
Data Mining: An Overview from a Databases Perspective
– Chen, Han, et al.
- 1996
|
|
259
|
Mining quantitative association rules in large relational tables
– Srikant, Agrawal
- 1996
|
|
209
|
Exploratory mining and pruning optimizations of constrained associations rules
– Ng, Lakshmanan, et al.
- 1998
|
|
192
|
Discovering frequent episodes in sequences
– Mannila, Toivonen, et al.
- 1995
|
|
174
|
Finding Interesting Rules from Large Sets of Discovered Association Rules
– Klemettinen, Mannila, et al.
- 1994
|
|
172
|
Mining association rules with item constraints
– Srikant, Vu, et al.
- 1997
|
|
147
|
An E ective Hash Based Algorithm for Mining Association Rules
– Park, Chen, et al.
- 1995
|
|
147
|
Maintenance of discovered association rules in large databases: an 356 incremental updating technique
– Cheung, Han, et al.
- 1996
|
|
115
|
Discovery of spatial association rules in geographic information databases
– Koperski, Han
- 1995
|
|
105
|
Constraint-based rule mining in large, dense databases
– Bayardo, Agrawal, et al.
|
|
103
|
SPADE: An Efficient Algorithm for Mining Frequent Sequences
– Zaki
- 2000
|
|
99
|
Bottom-up computation of sparse and iceberg cubes
– BEYER, R
- 1999
|
|
98
|
Automatic Construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery
– Murthy
- 1997
|
|
89
|
K.: SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
– Garofalakis, Rastogi, et al.
- 1999
|
|
81
|
Clustering Association Rules
– Lent, Swami, et al.
|
|
78
|
Survey of Clustering Data Mining Techniques
– Berkhin
- 2002
|
|
78
|
Efficient mining of partial periodic patterns in time series database
– Han, Dong, et al.
- 1999
|
|
68
|
Classification Algorithms
– James
- 1985
|
|
61
|
A General Incremental Technique for Maintaining Discovered Association Rules
– David, Lee, et al.
- 1997
|
|
60
|
Freespan: Frequent pattern-projected sequential pattern mining
– Han, Pei, et al.
- 2000
|
|
59
|
An Information Theoretic Approach to Rule Induction from Databases
– Smyth, Goodman
- 1992
|
|
57
|
An Efficient Algorithm for the Incre-mental Updation of Association Rules
– Thomas, Bodagala, et al.
|
|
48
|
GeoMiner: a system prototype for spatial data mining
– HAN, KOPERSKI, et al.
- 1997
|
|
46
|
Can We Push More Constraints into Frequent Pattern Mining
– Pei, Han
- 2000
|
|
42
|
Incremental and interactive sequence mining
– Parthasarathy, Zaki, et al.
- 1999
|
|
39
|
Meta-rule-guided mining of association rules in relational databases
– Fu, Han
- 1995
|
|
34
|
Mining Asynchronous Periodic Patterns in Time Series Data
– Yang, Wang, et al.
- 2000
|
|
34
|
Mining frequent patterns by pattern-growth:methodology and implications
– Han, Pei
- 2000
|
|
32
|
Mining long sequential patterns in a noisy environment
– Yang, Wang, et al.
- 2002
|
|
25
|
InfoMiner: mining surprising periodic patterns
– Yang, Wang, et al.
- 2001
|
|
18
|
Universal formulation of sequential patterns
– Joshi, Karypis, et al.
- 1999
|
|
16
|
Mining temporal relationships with multiple granularities in time sequences
– Bettini, Wang, et al.
- 1998
|
|
16
|
Mining Knowledge at Multiple Concept Levels
– Han
- 1995
|
|
15
|
Multi-dimensional sequential pattern mining
– Pinto, Han, et al.
- 2001
|
|
15
|
Maintenance of discovered association rules: When to update
– Lee, Cheung
- 1997
|
|
12
|
Stock movement prediction and n-dimensional intertransaction association rules
– Lu, Han, et al.
- 1998
|
|
10
|
Incremental Mining of Sequential Patterns in Large Databases
– Masseglia, Poncelet, et al.
- 2000
|
|
7
|
Efficient algorithms for incremental update of frequent sequences
– Zhang, Kao, et al.
- 2002
|
|
6
|
Efficient mining of weighted association rules (war
– Wang, Yang, et al.
- 2000
|
|
5
|
A GSP-based efficient algorithm for mining frequent sequences
– Zhang, Kao, et al.
- 2001
|
