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In the past, the multiple fuzzy frequent pattern tree (MFFP tree) was proposed for extracting multiple fuzzy frequent itemsets from quantitative transactions. It kept the multiple transformed fuzzy regions of an item to form the multiple fuzzy frequent itemsets. In this paper, an incremental algorithm is proposed for efficiently mining multiple fuzzy frequent itemsets based on the FUP concepts and...
The privacy-preserving data mining (PPDM) has become an important issue in recent years. In this paper, we propose a lattice-based approach for modifying original databases in order to hide sensitive itemsets. The lattice structure is built based on the relation of sensitive itemsets. The approach uses the bottom-up deletion strategies to gradually reduce the frequency of sensitive itemsets in the...
Many approaches for preserving association rule privacy, such as association rule mining outsourcing, association rule hiding, and anonymity, have been proposed. In particular, association rule hiding on single transaction table has been well studied. However, hiding multi-relational association rule in data warehouses is not yet investigated. This work presents a novel algorithm to hide predictive...
Current technology for association rules hiding mostly applies to data stored in a single transaction table. This work presents a novel algorithm for hiding sensitive association rules in data warehouses. A data warehouse is typically made up of multiple dimension tables and a fact table as in a star schema. Based on the strategies of reducing the confidence of sensitive association rule and without...
The average utility measure reveals a better utility effect of combining several items than the original utility measure. In this paper, we propose a two-phase average-utility mining algorithm that can incrementally maintain the high average-utility itemsets as a database grows. Based on the concept of the FUP algorithm, the proposed algorithm combines the previously mined information from the original...
In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on the ant colony systems. In that approach, the precision was limited since binary bits were adopted to encode the membership functions. The paper thus extends the original approach for increasing the accuracy of the results by adding multi-level processing. The membership functions...
We propose here an efficient data mining algorithm to hide collaborative recommendation association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicted item, a collaborative recommendation association rule set [10] is the smallest association rule set that makes the same recommendation as the entire association rule set by confidence...
In this paper, a new concept of up-to-date patterns is proposed, which is a hybrid of the association rules and temporal mining. An up-to-date pattern is composed of an item set and its up-to-date lifetime, in which the user-defined minimum support threshold must be satisfied. The proposed approach can mine more useful large itemsets than the conventional ones which discover large itemsets valid only...
Ant colony systems (ACS) have been successfully applied to optimization problems in recent years. However, few works have been done on applying ACS to data mining. This paper proposes an ACS-based algorithm to extract membership functions in fuzzy data mining. The membership functions are first encoded into binary bits and then fed into the ACS to search for the optimal set of membership functions...
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