scholarly journals Graded Galois Lattices and Closed Itemsets

2021 ◽  
Author(s):  
Reza Sotoudeh ◽  
Hamidreza Goudarzi ◽  
Ali Akbar Nikoukar
Keyword(s):  
Galois Lattices ◽  
Closed Itemsets

scholarly journals Dist Frequent Next Neighbours: A Distributed Galois Lattice Algorithm for Frequent Closed Itemsets Extraction

2021 ◽  
Author(s):  
Naomie Sandra Noumi Sandji ◽  
Djamal Abdoul Nasser Seck
Keyword(s):  
Big Data ◽  
Concept Lattice ◽  
General Purpose ◽  
Frequent Itemsets ◽  
Galois Lattice ◽  
Galois Lattices ◽  
Closed Itemsets ◽  
Efficient Alternative ◽  
Lattice Algorithm ◽  
Lattice Construction

The general purpose of this paper is to propose a distributed version of frequent closed itemsets extraction in the context of big data. The goal is to have good performances of frequent closed itemsets extraction as frequent closed item-sets are bases for frequent itemsets. To achieve this goal, we have extended the Galois lattice technique (or concept lattice) in this context. Indeed, Galois lattices are an efficient alternative for extracting closed itemsets which are interesting approaches for generating frequent itemsets. Thus we proposed Dist Frequent Next Neighbour which is a distributed version of the Frequent Next Neighbour concept lattice construction algorithm, which considerably reduces the extraction time by parallelizing the computation of frequent concepts (closed itemsets).

scholarly journals TripleEye: Mining Closed Itemsets with Minimum Length Thresholds Based on Ordered Inclusion Tree

2012 ◽  
Vol 5 (0) ◽  
pp. 192-202
Author(s):  
Hiroyuki Shindo ◽  
Tsutomu Hirao ◽  
Jun Suzuki ◽  
Akinori Fujino ◽  
Masaaki Nagata
Keyword(s):  
Minimum Length ◽  
Closed Itemsets

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

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.

scholarly journals MINING ESSENTIAL RULES USING FREQUENT CLOSED ITEMSETS LATTICE

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.

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.

Mining Frequent Closed Itemsets from Distributed Repositories

2007 ◽  
pp. 221-234 ◽  
Author(s):  
Claudio Lucchese ◽  
Salvatore Orlando ◽  
Raffaele Perego ◽  
Claudio Silvestri
Keyword(s):  
Closed Itemsets
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