Association rule mining using a bacterial colony algorithm

Cunha, Danilo Souza da; Xavier, Rafael Silveira; Ferrari, Daniel Gomes; Castro, Leandro Nunes de

doi:10.1109/la-cci.2015.7435950

Cited by 5 publications

(9 citation statements)

References 22 publications

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“…It is an evidence of how frequently the items appear in the database or in set of transactions. [4] (3) Where X is item set in a transaction and T is total number of the transactions.…”

Section: Supportmentioning

confidence: 99%

“…Confidence: [4] It interprets that X→ Y rule with detail to a set of transactions is the ratio of the transactions that covers X also comprises Y. It is likewise characterized:…”

Section: Supportmentioning

confidence: 99%

“…These nature-inspired methodologies neglect the ideal neighborhood arrangement and spotlight on meta-heuristics to locate the most ideal arrangement. There are different types of various algorithms have been established that signify the stimulation for the optimization of particle swarms [2], the optimization of the colony of ants [6], the bat [7], the search for the cuckoo [8], l Optimization of bacterial colonies [4] and the wolf research optimization [9].…”

Section: Related Work With Optimizing Algorithmsmentioning

confidence: 99%

“…The parameter length is a marker of standard's unpredictability (bigger length shows progressively complex rules) which is equivalent to the quantity of things in precursor. [12] …… (6) Equation (2 )is the fitness function used in Bacterial colony optimization where w1 = w2 and w1 + w2 =1 [4].…”

Section: ………(5)mentioning

confidence: 99%

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Extraction of Association Rule Mining using Apriori algorithm with Wolf Search Optimisation in R Programming

2019

ijrte

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Association rules mining (ARM) is a standout amongst the most essential Data Mining Systems. Find attribute patterns as a binding rule in a data set. The discovery of these suggestion rules would result in a mutual method. Firstly, regular elements are produced and therefore the association rules are extracted. In the literature, different algorithms inspired by nature have been proposed as BCO, ACO, PSO, etc. to find interesting association rules. This article presents the performance of the ARM hybrid approach with the optimization of wolf research based on two different fitness functions. The goal is to discover the best promising rules in the data set, avoiding optimal local solutions. The implementation is done in numerical data that require data discretization as a preliminary phase and therefore the application of ARM with optimization to generate the best rules.

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“…It is an evidence of how frequently the items appear in the database or in set of transactions. [4] (3) Where X is item set in a transaction and T is total number of the transactions.…”

Section: Supportmentioning

confidence: 99%

“…Confidence: [4] It interprets that X→ Y rule with detail to a set of transactions is the ratio of the transactions that covers X also comprises Y. It is likewise characterized:…”

Section: Supportmentioning

confidence: 99%

Section: Related Work With Optimizing Algorithmsmentioning

confidence: 99%

Section: ………(5)mentioning

confidence: 99%

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Extraction of Association Rule Mining using Apriori algorithm with Wolf Search Optimisation in R Programming

2019

ijrte

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“…The bacterial colony algorithm offered better results in data mining. But it required some other bio-inspired algorithms for solving the issues in real world data mining and optimization [14].…”

Section: International Journal Of Recent Technology and Engineering (mentioning

confidence: 99%

An Optimization based Algorithm for Association Rule Mining in Large Databases

Kala*¹

2019

IJRTE

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In recent trends, the information and science technology has been developed which makes most of the research in Association Rule Mining (ARM) to be focused on enhancing the computational efficiency.But the searching for most appropriate data in large databases become more difficult.The major difficulties in this is determining the threshold values for confidence and support which causes effect on the quality of the association rule mining. Thus a novel optimization based algorithm for association rule mining is proposed in this work for the purpose of enhancing the computational efficiency and also to determine the appropriate threshold values automatically. Initially the input raw data is taken from the large transactional database.Then this input raw data is preprocessed and converted into binary type data.After this, the Modified Binary Particle Swarm Optimization (MBPSO) algorithm is used which involves five phases namely 1) Encoding – where a string based encoding is followed that encodes every single item into respective string chromosome, 2) Fitness Evaluation – where the ‘maximization’ has been determined using association rule type parameters, 3) Population generation – where an initial population has been generated using particles with best fitness values, 4) Searching – where best particles are searched in order to prevent it from falling apart from search space whenever the position is updated, 5) Stopping circumstance – as it is essential to complete the particle evaluation, by finding respective confidence and support as the minimal threshold values. This would be helpful in deriving valuable information in ARM. The performance of this proposed methodology is validated and compared with the tradition association rule mining algorithms. This proposed methodology offers better results with increased efficiency and proves its superiority.

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