Mining Positive and Negative Association Rules from Frequent and Infrequent Pattern Using Improved Genetic Algorithm

Jain, Jeetesh Kumar; Tiwari, Nirupama; Ramaiya, Manoj

doi:10.1109/cicn.2013.146

Cited by 4 publications

(2 citation statements)

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“…Confidence is likewise a probability, but confidence has a condition attached to it. Confidence, if a transaction includes X, it also includes Y [8][9][10]. In association rule mining, the following two steps are used to generate rules: 1) All frequent item sets are located with the least amount of assistance.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Association Rule Mining and its Algorithms: Apriori, FP Growth, and SS-FIM

Vashi

2023

IJRASET

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The technique of extracting usable information from databases is known as data mining. It is employed to manage massive amounts of data. In order to forecast future trends and behaviours, data mining is performed. Today, data mining is a technique that is growing helpful since it has many uses. Massive amounts of data are produced daily in many different fields. Data mining is utilised since it is challenging to analyse such a big volume of data. It is often referred to as database knowledge discovery (KDD). Data mining technologies are used to forecast the behaviour and pattern of the data, assisting organisations in decision-making [1]. Association Rule Mining is one of the data mining techniques discussed in the present article. It has three algorithms. 1. Apriori, 2. FP-Growth, and 3. SS-FIM. In the current work, all three algorithms are discussed. After examining each algorithm, it was determined that the SS-FIM algorithm had the best combination of features

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Association Rule Mining and its Algorithms: Apriori, FP Growth, and SS-FIM

Vashi

2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…Word index for string extension candidates and queue initial state as shown in figure 2. In this example, whether word "A" can be an frequent pattern with its following word "B", we go to judge their association strength using language rules, and the evaluation criteria of Confidence and R-confidence [14]. Confidence refers to the conditional probability of the below word W i of word association W i-1 →W i in the case that it's above word W i-1 is appeared.…”

Section: Figure 1 Index Samplementioning

confidence: 99%