Research on an Algorithm for Mining Frequent Closed Itemsets

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

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Frequent closed itemsets lattice used in data mining

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826) ◽

10.1109/icmlc.2004.1382057 ◽

2005 ◽

Author(s):

Zhi-Hua Cheng ◽

Lei Jia ◽

Ren-Qing Pei

Keyword(s):

Data Mining ◽

Closed Itemsets

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MINING ESSENTIAL RULES USING FREQUENT CLOSED ITEMSETS LATTICE

Science and Technology Development Journal ◽

10.32508/stdj.v12i11.2311 ◽

2009 ◽

Vol 12 (11) ◽

pp. 49-56

Author(s):

Bac Hoai Le ◽

Bay Dinh Vo

Keyword(s):

Association Rules ◽

Frequent Itemsets ◽

Suitable Method ◽

Mining Method ◽

Parent Child Relationship ◽

Left Hand ◽

Child Relationship ◽

Closed Itemsets ◽

The Cost ◽

Parent Child

In traditional mining of association rules, finding all association rules from databases that satisfy minSup and minConf faces with some problems in case of the number of frequent itemsets is large. Thus, it is necessary to have a suitable method for mining fewer rules but they still embrace all rules of traditional mining method. One of the approaches that is the mining method of essential rules: it only keeps the rule that its left hand side is minimal and its right side is maximal (follow in parent-child relationship). In this paper, we propose a new algorithm for mining the essential rules from the frequent closed itemsets lattice to reduce the time of mining rules. We use the parent-child relationship in lattice to reduce the cost of considering parent-child relationship and lead to reduce the time of mining rules.

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Mining Closed Item sets using Partition based Single Scan Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a1920.078219 ◽

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.

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