Data Access Paths in Processing of Sets of Frequent Itemset Queries
نویسندگان
چکیده
Frequent itemset mining can be regarded as advanced database querying where a user specifies the dataset to be mined and constraints to be satisfied by the discovered itemsets. One of the research directions influenced by the above observation is the processing of sets of frequent itemset queries operating on overlapping datasets. Several methods of solving this problem have been proposed, all of them assuming selective access to the partitions of data determined by the overlapping of queries, and tested so far only on flat files. In this paper we theoretically and experimentally analyze the influence of data access paths available in database systems on the methods of frequent itemset query set processing, which is crucial from the point of view of their possible applications.
منابع مشابه
Data Access Paths for Frequent Itemsets Discovery
Many frequent itemset discovery algorithms have been proposed in the area of data mining research. The algorithms exhibit significant computational complexity, resulting in long processing times. Their performance is also dependent on source data characteristics. We argue that users should not be responsible for choosing the most efficient algorithm to solve a particular data mining problem. In...
متن کاملThree Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth
Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. Recently, a new problem of optimizing processing of sets of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Mine Merge and Common Counting have been proposed and ...
متن کاملEfficient Processing of Streams of Frequent Itemset Queries
Frequent itemset mining is one of fundamental data mining problems that shares many similarities with traditional database querying. Hence, several query optimization techniques known from database systems have been successfully applied to frequent itemset queries, including reusing results of previous queries and multi-query optimization. In this paper, we consider a new problem of processing ...
متن کاملIntegrated Candidate Generation in Processing Batches of Frequent Itemset Queries using Apriori
Frequent itemset mining can be regarded as advanced database querying where a user specifies constraints on the source dataset and patterns to be discovered. Since such frequent itemset queries can be submitted to the data mining system in batches, a natural question arises whether a batch of queries can be processed more efficiently than by executing each query individually. So far, two method...
متن کاملA Study of Differentially Private Frequent Itemset Mining
Frequent sets play an important role in many Data Mining tasks that try to search interesting patterns from databases, such as association rules, sequences, correlations, episodes, classifiers and clusters. FrequentItemsets Mining (FIM) is the most well-known techniques to extract knowledge from dataset. In this paper differential privacy aims to get means to increase the accuracy of queries fr...
متن کامل