Optimization of association rule mining using improved genetic algorithms

Saggar, Manish; Agrawal, Anupam; Lad, Abhimanyu

doi:10.1109/icsmc.2004.1400923

Cited by 46 publications

(24 citation statements)

References 3 publications

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“…An itemset X ⊆ J is frequent if at least a fraction sprt() of the transaction in a database contains X. Frequent itemsets are important because they are the building blocks to obtain association rules with a given confidence and support [1][2].…”

Section: Association Rulementioning

confidence: 99%

“…Each instance (world) is associated with the probability that the world is "true". The probabilities reflect the probability distribution of all possible database instances.The approach proposed was the first approach able to solve probabilistic queries efficiently under tuple independency by means of dynamic programming techniques [1][2] and [4].…”

Section: Related Work On Frequent Itemset Miningmentioning

confidence: 99%

“… Frequent itemset: This is an itemset that has minimum support.  Candidate set: This is the name given to a set of itemsets that require testing to see if they fit a certain requirement [1] and [5].…”

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confidence: 99%

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An Efficient Fuzzy Association Rule Mining Technique

Rajput¹,

Sharma²,

Swami³

2015

International Journal of Advanced Research in Computer and Comm

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Abstract-Association rules mining in large databases is a core topic of data mining. Discovering these associations is beneficial to the correct and appropriate decision made by decision makers. Discovering frequent item sets is the key process in association rule mining. One of the challenges in developing association rules mining algorithms is the extremely large number of rules generated which makes the algorithms inefficient and makes it difficult for the end users to comprehend the generated rules. In this paper we proposed efficient fuzzy association rule mining technique to find all co-occurrence relationships among data items. The proposed method which allows considerably reduced the search space with discover the frequent item set and finding fuzzy sets for quantitative attributes in a database and finally employs techniques for mining of Fuzzy Associate Rules Mining (FARM).

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Section: Association Rulementioning

confidence: 99%

Section: Related Work On Frequent Itemset Miningmentioning

confidence: 99%

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An Efficient Fuzzy Association Rule Mining Technique

Rajput¹,

Sharma²,

Swami³

2015

International Journal of Advanced Research in Computer and Comm

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“…Manish Saggar et al [12] used the Genetic Algorithms to optimize the rules generated by Association Rule Mining(Apriori method). In general, the rule generated by Association Rule Mining technique do not consider the negative occurrences of attributes in them, but by applying Genetic Algorithms (GAs) over these rules, the system can predict the rules which contains negative attributes.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Survey of Genetic Algorithm for Association Rule Mining

Kaur¹,

Aggarwal²

2013

IJCA

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“…Selecting better rules from them may be another problem. After detecting the frequent item-sets in the first phase, the second phase generates the rules using another user defined parameter called minimum confidence [2] and [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A survey of Anomaly Detection using Frequent Item Sets

Shelke¹,

Jain²,

Dubey³

2013

IJCATR

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Knowledge extraction is a process of filtering some informative knowledge from the database so that it can be used wide variety of applications and analysis. Due to this highly efficient algorithm is required for data mining and for accessing data from large datasets. In frequent item sets are produced from very big or huge data sets by applying some rules or association rule mining algorithms like Apriori technique, Partition method, Pincer-Search, Incremental, Border algorithm and many more, which take larger computing time to calculate all the frequent itemsets. As the network traffic increases we need an efficient system to monitor packet analysis of network flow data. Due to this frequent itemsets mining is basic problem in field of data mining and knowledge discovery. Here in this paper a brief survey of all the techniques related to frequent item sets generation has been given.

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Optimization of association rule mining using improved genetic algorithms

Cited by 46 publications

References 3 publications

An Efficient Fuzzy Association Rule Mining Technique

An Efficient Fuzzy Association Rule Mining Technique

A Survey of Genetic Algorithm for Association Rule Mining

A survey of Anomaly Detection using Frequent Item Sets

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