Top Down Approach to find Maximal Frequent Item Sets using Subset Creation

Jnanamurthy, H K; Vishesh, H V; Jain, Vishruth; Kumar, Preetham; Pai, Radhika M.

doi:10.5121/csit.2012.2443

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Jaishree et al (2013) explained transaction reduction method to improve efficiency. Jnanamurthy et al (2013) discussed mining maximal frequent item sets using subset creation. In all the above said and new improvements, the thing which cannot be avoided is the generation of huge volume of candidate sets.…”

Section: Related Workmentioning

confidence: 99%

A Combintorial Tree Based Frequent Pattern Mining

Devi¹

2014

Journal of Computer Science

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Frequent pattern mining is a process of extracting frequently occurring itemset patterns from very large data storages. These frequent patterns are used to generate association rules which define the relationship among items. The strength of the relationship can be measured using two different units namely support value and confidence level. Any relationship that satisfies minimum threshold of support value is known as frequent pattern. There are several methods and algorithms suggested to mine frequent patterns from large databases. Most of the methods can be assessed for its complexity based on the number of processing levels and number of candidate sets with subsets that are generated in each level. In this study, the combinatorial approach which generates minimal number of combinations using a tree structure and automatically filters infrequent itemsets and mine frequent patterns is suggested. It scans input database once and carries out minimized intersections to count the support value. The complexity is based on the number of transactions and the maximum length of transactions. The new approach purely depends on the size of input transaction database. The combinatorial approach does not depend on the unknown number of processing levels and there is nocandidate sets and subsets generation. The proposed method makes minimal number of combinations when compared to number of candidate sets and subsets in other methods. The method is compared with number of existing legendary methods for its performance.

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Section: Related Workmentioning

confidence: 99%

A Combintorial Tree Based Frequent Pattern Mining

Devi¹

2014

Journal of Computer Science

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“…mark in the ith position of a transaction means that we do not have any information regarding whether the transaction contains the ith item or not. Instead of a single value for the support of an itemset A, the support interval, [minsup value (A); maxsup value (A)] where the actual support of itemset [14] A can be any value between minsup value (A) and also maxsup value (A). The minsup value (A) is the percentage of the transactions that contain 1's for all the items in A and maxsup value (A) is the percentage of the transactions that contain in either 1 mark for all the items in A.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Implementation of Efficient Association Rules using K-mean and Neuralgas Algorithm

Patel¹,

Richariya²,

Shrivastava³

2013

IOSR-JCE

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Extracting hidden patterns from dates' product data using a machine learning technique

Al-Hagery

2019

IJ-AI

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<div style="’text-align: justify;">Mining in data is an important step for knowledge discovery, which leads to extract new patterns from datasets. It is a widespread methodology that has the capability to help ministries, companies, and experts for diving into the data to find important insights and patterns to help them take suitable decisions. The farmers and marketers of the date product in the production regions lack to discover the most important characteristics of dates types from the economically, healthy, and the type of consumers point of view to achieve the highest profits by choosing the best types and the most consumed. The research objective is to extract interesting patterns from the dates’ product dataset, using Machine Learning, based on association rules generation. This, in turn, will support the farmers, and marketers to discover new features related to the production, consumption, and marketing processes. This research used a real dataset collected from KSA, Qassim region, which is the first region of cultivation of palm, that produces the best types of dates in the Arab region. The data preprocessed and analyzed by the Apriori algorithm. The results show important features and insights related to the health benefits of dates, production, its consumption, consumers types, and marketing. Consequently, these results can be employed, for instance, to encourage individuals to consume dates for their nutritional value and their important health benefits., furthermore, the results encourage producers to focus on the production of preferable types and to improve the marketing policies of the other types.</div>

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Top Down Approach to find Maximal Frequent Item Sets using Subset Creation

Abstract: ABSTRACT

Cited by 3 publications

References 5 publications

A Combintorial Tree Based Frequent Pattern Mining

A Combintorial Tree Based Frequent Pattern Mining

Analysis and Implementation of Efficient Association Rules using K-mean and Neuralgas Algorithm

Extracting hidden patterns from dates' product data using a machine learning technique

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