PPQAR: Parallel PSO for quantitative association rule mining
نویسندگان
چکیده
منابع مشابه
Parallel Association Rule Mining on Heterogeneous System
Association Rule Mining from transaction–oriented databases is one of the important process that finds relation between items and plays important role in decision making. Parallel algorithms are required because of large size of the database to be mined. Most of the algorithms designed were for homogeneous system uses static load balancing technique which is far from reality. A parallel algorit...
متن کاملAlgorithms for Association Rule Mining
Association Rule Mining (ARM) is one of the important data mining tasks that has been extensively researched by data-mining community and has found wide applications in industry. An Association Rule is a pattern that implies co-occurrence of events or items in a database. Knowledge of such relationships in a database can be employed in strategic decision making in both commercial and scientific...
متن کاملData sanitization in association rule mining based on impact factor
Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved ag...
متن کاملTowards Healthy Association Rule Mining (HARM): A Fuzzy Quantitative Approach
Association Rule Mining (ARM) is a popular data mining technique that has been used to determine customer buying patterns. Although improving performance and efficiency of various ARM algorithms is important, determining Healthy Buying Patterns (HBP) from customer transactions and association rules is also important. This paper proposes a framework for mining fuzzy attributes to generate HBP an...
متن کاملFast Parallel Association Rule Mining without Candidacy Generation
In this paper we introduce a new parallel algorithm MLFPT (Multiple Local Frequent Pattern Tree) [11] for parallel mining of frequent patterns, based on FP-growth mining, that uses only two full I/O scans of the database, eliminating the need for generating the candidate items, and distributing the work fairly among processors. We have devised partitioning strategies at different stages of the ...
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ژورنال
عنوان ژورنال: Peer-to-Peer Networking and Applications
سال: 2019
ISSN: 1936-6442,1936-6450
DOI: 10.1007/s12083-018-0698-1