Efficient Algorithm for Discovering Potential Interesting Patterns with Closed Itemsets

2010 ◽  
Author(s):  
Raj Singh ◽  
Tom Johnsten ◽  
Vijay Raghavan ◽  
Ying Xie
Keyword(s):  
Efficient Algorithm ◽  
Closed Itemsets

scholarly journals Mining Closed Item sets using Partition based Single Scan Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 3885-3889
Keyword(s):  
Efficient Algorithm ◽  
Empirical Evaluation ◽  
Frequent Itemsets ◽  
Frequent Item ◽  
Closed Itemsets ◽  
Frequent Item Sets

Closed item sets are frequent itemsets that uniquely determines the exact frequency of frequent item sets. Closed Item sets reduces the massive output to a smaller magnitude without redundancy. In this paper, we present PSS-MCI, an efficient candidate generate based approach for mining all closed itemsets. It enumerates closed item sets using hash tree, candidate generation, super-set and sub-set checking. It uses partitioned based strategy to avoid unnecessary computation for the itemsets which are not useful. Using an efficient algorithm, it determines all closed item sets from a single scan over the database. However, several unnecessary item sets are being hashed in the buckets. To overcome the limitations, heuristics are enclosed with algorithm PSS-MCI. Empirical evaluation and results show that the PSS-MCI outperforms all candidate generate and other approaches. Further, PSS-MCI explores all closed item sets.

FCILINK: Mining Frequent Closed Itemsets Based on a Link Structure between Transactions

2005 ◽  
Vol 04 (04) ◽  
pp. 257-267
Author(s):  
Kyong Rok Han ◽  
Jae Yearn Kim
Keyword(s):  
Association Rules ◽  
Efficient Algorithm ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Link Structure ◽  
The Past ◽  
Large Databases ◽  
Closure Mechanism ◽  
Closed Itemsets ◽  
Significant Patterns

The problem of discovering association rules between items in a database is an emerging area of research. Its goal is to extract significant patterns or interesting rules from large databases. Recent studies of mining association rules have proposed a closure mechanism. It is no longer necessary to mine the set of all of the frequent itemsets and their association rules. Rather, it is sufficient to mine the frequent closed itemsets and their corresponding rules. In the past, a number of algorithms for mining frequent closed itemsets have been based on items. In this paper, we use the transaction itself for mining frequent closed itemsets. An efficient algorithm called FCILINK is proposed that is based on a link structure between transactions. A given database is scanned once and then a much smaller sub-database is scanned twice. Our experimental results show that our algorithm is faster than previously proposed methods. Furthermore, our approach is significantly more efficient for dense databases.

An efficient algorithm for incrementally mining frequent closed itemsets

2013 ◽  
Vol 40 (4) ◽  
pp. 649-668 ◽  
Author(s):  
Show-Jane Yen ◽  
Yue-Shi Lee ◽  
Chiu-Kuang Wang
Keyword(s):  
Efficient Algorithm ◽  
Closed Itemsets

TFP: an efficient algorithm for mining top-k frequent closed itemsets

2005 ◽  
Vol 17 (5) ◽  
pp. 652-663 ◽  
Author(s):  
Jianyong Wang ◽  
J. Han ◽  
Y. Lu ◽  
P. Tzvetkov
Keyword(s):  
Efficient Algorithm ◽  
Closed Itemsets

An Efficient Algorithm for Mining Frequent Closed Itemsets over Data Stream

2012 ◽  
Vol 151 ◽  
pp. 570-575
Author(s):  
Guo Dong Li ◽  
Ke Wen Xia
Keyword(s):  
Data Mining ◽  
Data Structure ◽  
Data Streams ◽  
Efficient Algorithm ◽  
Data Stream ◽  
New Method ◽  
Closed Itemsets ◽  
Low Efficiency

Aiming at the problem of NewMoment algorithm frequently do leftcheck operation in the data mining process, which leads to the low efficiency of algorithm. In this paper, a new method, called LevelMoment, is proposed to improve the NewMoment algorithm which mines frequent closed itemsets over data streams. In this process, a new data structure that added in level node, called LevelCET, is proposed. On this structure, using level checking strategy and optimum frequent closed items checking strategy can quickly tap all the frequent closed itemsets over data streams. The experiments and analysis show that the algorithm has good performance.

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