Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Customer Retention via Data Mining
Artificial Intelligence Review - Issues on the application of data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Post-mining: maintenance of association rules by wieghting
Information Systems
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Study of the stability of a standing posture using a dynamics model of paraplegia
Systems and Computers in Japan
Information Sciences—Informatics and Computer Science: An International Journal
Bifold Constraint-Based Mining by Simultaneous Monotone and Anti-Monotone Checking
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Preliminary Study on Constructing Decision Tree with Gene Expression Programming
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
On developing system dynamics model for business process simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
A New Generalized Particle Dynamics Model For Software Cybernetics
COMPSAC '06 Proceedings of the 30th Annual International Computer Software and Applications Conference - Volume 02
Self-Organizing Software Processes Based on Particle Dynamics Model
COMPSAC '06 Proceedings of the 30th Annual International Computer Software and Applications Conference - Volume 02
Determinants of e-repurchase intentions: An integrative model of quadruple retention drivers
Information and Management
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
Journal of Management Information Systems
Discovering frequent geometric subgraphs
Information Systems
Toward a hybrid data mining model for customer retention
Knowledge-Based Systems
Theoretical advantages of lenient Q-learners: an evolutionary game theoretic perspective
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Mining interesting imperfectly sporadic rules
Knowledge and Information Systems
An approach to mining bundled commodities
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Exploiting processing locality through paging configurations in multitasked reconfigurable systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Fuzzy control of inverted pendulum and concept of stability using Java application
Mathematical and Computer Modelling: An International Journal
Trie: An alternative data structure for data mining algorithms
Mathematical and Computer Modelling: An International Journal
Two-aircraft formation flight simulation system based on four-tiered architecture
Computers & Mathematics with Applications
| Hi-index | 12.05 |
Many association rules with low supports and high confidence are commonly not convincing, so how to enhance the conviction of such rules is a big issue. In this paper, we explore the dynamics features of the domain dataset, and by predefining some dynamics parameters (as priori knowledge), we construct a proximate dynamics model for data mining so as to enhance the conviction of the rules with low supports. For constructing such dynamics model for data mining, we adopt three techniques: (1) a large domain dataset is classified into several sub-clusters, which we regard as proximate dynamics systems; (2) the data mining process is a solving process of differential equations, which captures the changes of the data, not only the values themselves; and (3) a weighting method is used to synthesize the local mining results with the users' preferences. Although we ''arbitrarily'' apply the dynamics parameters into the sub-clusters of the given dataset, the experimental results are very well by comparing with FP-growth algorithm and CLOSET+ algorithm. Experiments conducted on the distributed network with three real life datasets show that the proposed method can discover the knowledge based on dynamics, which is potentially useful for mining the rules with low supports and high confidence.