Exploratory mining via constrained frequent set queries
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Efficient Data Mining Technique for Discovering Interesting Association Rules
DEXA '97 Proceedings of the 8th International Workshop on Database and Expert Systems Applications
Using data mining to increase customer life time value
AIC'06 Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications
An approach to mining bundled commodities
Knowledge-Based Systems
Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection
Expert Systems with Applications: An International Journal
Mining the change of event trends for decision support in environmental scanning
Expert Systems with Applications: An International Journal
Mining important association rules based on the RFMD technique
International Journal of Data Analysis Techniques and Strategies
Discovering competitive intelligence by mining changes in patent trends
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules and sequential patterns from a large database, and users may be only interested in some information. Moreover, the criteria of the discovered association rules and sequential patterns for the user requirements may not be the same. Many uninteresting information for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the association rules or sequential patterns to be discovered. Also, the efficient data mining techniques are proposed to extract the association rules and the sequential patterns according to the user requirements.