Sequential Association Rule Mining with Time Lags
Journal of Intelligent Information Systems
Go Green: Recycle and Reuse Frequent Patterns
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Empirical likelihood confidence intervals for differences between two datasets with missing data
Pattern Recognition Letters
GAM: a guidance enabled association mining environment
International Journal of Business Intelligence and Data Mining
A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
Estimating confidence intervals for structural differences between contrast groups with missing data
Expert Systems with Applications: An International Journal
Difference detection between two contrast sets
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Mining frequent itemsets with dualistic constraints
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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Mining of frequent itemsets is a fundamental datamining task. Past research has proposed many efficientalgorithms for the purpose. Recent work also highlightedthe importance of using constraints to focus the miningprocess to mine only those relevant itemsets. In practice,data mining is often an interactive and iterative process.The user typically changes constraints and runs the miningalgorithm many times before satisfied with the finalresults. This interactive process is very time consuming.Existing mining algorithms are unable to take advantageof this iterative process to use previous mining results tospeed up the current mining process. This results inenormous waste in time and in computation. In this paper,we propose an efficient technique to utilize previousmining results to improve the efficiency of current miningwhen constraints are changed. We first introduce theconcept of tree boundary to summarize the usefulinformation available from previous mining. We then showthat the tree boundary provides an effective and efficientframework for the new mining. The proposed techniquehas been implemented in the contexts of two existingfrequent itemset mining algorithms, FP-tree and TreeProjection. Experiment results on both synthetic and real-lifedatasets show that the proposed approach achievesdramatic saving in computation.