Ao, F., Yan, Y., Huang, J., Huang, K. "Mining maximal frequent item sets in data streams based on FP trees" Springer Verlag Berlin Heidelberg pp. 479-489, (2007).Ao FJ, Yan YJ, et al. Mining maximal frequent itemsets in data streams based on FP-Tree[C]//Proc. of the 5th ...
Mining frequent item set (FI) is an important issue in data mining. Considering the limitations of those exact algorithms and sampling methods, a novel FI mining algorithm based on granular computing and fuzzy set theory (FI-GF) is proposed, which mines those datasets with high number of trans...
MrFIM: A MapReduce Approach for Frequent Itemset Mining in Big Data Nowadays simultaneous extraction algorithms for finding common element sets does not allows concurrent execution, load steadiness, data allocation, and rec... A Rahman,A Manjaramkar - International Conference for Convergence in ...
ing the frequencies of many itemsets at once in short time. In an iteration of mining algorithms, some candidate item- sets are generated, and their frequencies are computed. The occurrence deliver stores the transaction database by ar-
Efficient algorithm for extreme maximal biclique mining in cognitive frequency decision making Based on a well-known LCM (Linear time Closed itemset Miner) algorithm which enumerates frequent item sets (maximal bicliques), and using a new ... ZJ Fan,MX Liao,XX He,... - IEEE International Co...
FIM cannot satisfy the requirement of users who desire to discover item sets with high utilities such as high profits. The utility represents the importance of the product and interestedness of the users. Moreover in HUI, even though the large number of results with high utility were produced,...
data completely. KEYWORDS: Frequent item sets, Uncertain databases, Existential probability, Apriori algorithm, FP-tree, Incremental mining. 1. Introduction Knowledge discovery in databases (KDD) is to identify efficient and helpful information from large databases. Many techniques have been proposed for...
As we know that the online mining of streaming data is one of the most important issues in data mining. In this paper, we proposed an efficient one- .frequent item sets over a transaction-sensitive sliding window), to mine the set of all frequent item sets in data streams with a transac...
In addition, it reviews some of the most important algorithmic techniques and data structures that were developed to make the search for frequent item sets as efficient as possible. 2012 Wiley Periodicals, Inc. 展开 关键词: Association Rules Data Mining Software Tools ...
Jin, R., Agrawal, G.: An algorithm for in-core frequent itemset mining on streaming data. In: Proc. IEEE ICDM, pp. 210–217 (2005) Google Scholar Lakshmanan, L.V.S., Leung, C.K.-S., Ng, R.T.: Efficient dynamic mining of constrained frequent sets. ACM TODS 28(4), 337–38...