With wide applications of computers and automated data collection tools, massive amounts of data have been continuously collected and stored in databases, which creates an imminent need and great opportunities for mining interesting knowledge from data. Association rule mining is one kind of data mining techniques which discovers strong association or correlation relationships among data. The discovered rules may help market basket or cross-sales analysis, decision making, and business management. In this thesis, we propose and develop an interesting association rule mining approach, called on-line analytical mining of association rules, which integrates the recently developed OLAP (on-line analytical processing) technology with some efficient association mining methods. It leads to flexible, multi-dimensional, multi-level association rule mining with high performance. Several algorithms are developed based on this approach for mining various kinds of associations in multi-dimensional databases, including intra-dimensional association, inter-dimensional association, hybrid association, and constraints-based association. These algorithms have been implemented in the DBMiner system. Our study shows that this approach presents great advantages over many existing algorithms in terms of both flexibility and efficiency.
|
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
|
|
659
|
Mining sequential patterns
– Agrawal, Srikant
- 1995
|
|
502
|
Data cube: A relational aggregation operator generalizing group-by, and sub-totals
– Gray, Bosworth, et al.
- 1996
|
|
377
|
Implementing Data Cubes Efficiently
– Harinarayan, Rajaraman, et al.
- 1996
|
|
358
|
Mining generalized association rules
– Srikant, Agrawal
- 1995
|
|
322
|
Beyond market basket: Generalizing association rules to correlations
– Brin, Motwani, et al.
|
|
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
|
|
272
|
Sampling Large Databases for Association Rules
– Toivonen
- 1996
|
|
259
|
The data warehouse toolkit
– Kimball, Ross
- 2002
|
|
259
|
Mining quantitative association rules in large relational tables
– Srikant, Agrawal
- 1996
|
|
252
|
Efficiently mining long patterns from databases
– Bayardo
- 1998
|
|
209
|
Exploratory mining and pruning optimizations of constrained associations rules
– Ng, Lakshmanan, et al.
- 1998
|
|
201
|
An overview of data warehousing and olap technology
– Chaudhuri, Dayal
- 1997
|
|
186
|
On the computation of multidimensional aggregates
– Agarwal, Agrawal, et al.
- 1996
|
|
174
|
Finding Interesting Rules from Large Sets of Discovered Association Rules
– Klemettinen, Mannila, et al.
- 1994
|
|
174
|
An Effective Hash-Based Algorithm for Mining Association Rules
– Park, Chen, et al.
- 1995
|
|
172
|
Mining association rules with item constraints
– Srikant, Vu, et al.
- 1997
|
|
147
|
Maintenance of discovered association rules in large databases: an 356 incremental updating technique
– Cheung, Han, et al.
- 1996
|
|
145
|
An Array-Based Algorithm for Simultaneous Multidimensional Aggregations
– Zhao, Deshpande, et al.
- 1997
|
|
125
|
Scalable Parallel Data Mining for Association Rules
– Han, Karypis, et al.
- 1997
|
|
121
|
Psaila G., A New SQL-like Operator for Mining Association Rules
– Ceri, Meo
- 1996
|
|
105
|
Constraint-based rule mining in large, dense databases
– Bayardo, Agrawal, et al.
|
|
104
|
Data Mining Using TwoDimensional Optimized Association Rules: Scheme, Algorithms and Visualization
– Fukuda, Morimoto, et al.
|
|
81
|
Clustering Association Rules
– Lent, Swami, et al.
|
|
78
|
Efficient mining of partial periodic patterns in time series database
– Han, Dong, et al.
- 1999
|
|
59
|
Scalable techniques for mining causal structures
– Silverstein, Brin, et al.
- 1998
|
|
57
|
Metarule-guided mining of multi-dimensional association rules using data cubes
– KAMBER, HAN, et al.
- 1997
|
|
57
|
Association Rules Over Interval Data
– Miller, Yang
- 1997
|
|
56
|
Cyclic association rules
– Özden, Ramaswamy, et al.
- 1998
|
|
52
|
The Quest data mining system
– Agrawal, Mehta, et al.
- 1996
|
|
50
|
MineSet: An integrated system for data mining
– Brunk, Kelly, et al.
- 1997
|
|
49
|
Dynamic generation and refinement of concept hierarchies for knowledge discovery
– Han, FU
- 1994
|
|
27
|
On the discovery of interesting patterns in association rules
– Ramaswamy, Mahajan, et al.
- 1998
|
|
21
|
Ratio rules: A new paradigm for fast, quantifiable data mining
– Korn, Labrinidis, et al.
- 1998
|
|
18
|
DBMiner: A system for data mining in relational databases and data warehouses
– Han, Chiang, et al.
- 1997
|
|
17
|
Beyond decision support
– Codd, Codd, et al.
- 1993
|
|
14
|
Using metaqueries to integrate inductive learning and deductive database technology
– Shen, Mitbander, et al.
- 1994
|
|
10
|
Efficient parallel mining for association rules
– Park, Chen, et al.
- 1995
|
|
1
|
Intelligent Miner User's Guide, Version 1, Release 1
– IBM
- 1996
|
|
1
|
OLEDB for OLAP programmer's reference version 1.0
– Microsoft
- 1998
|
|
