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Association Rule Mining for Business Intelligence

   

Added on  2020-03-04

17 Pages4262 Words47 Views
Data Science and Big Data
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Running head: ASSOCIATION RULEAssociation Rule in Business Intelligence[Name of student][Name of University]
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1 ASSOCIATION RULEAbstractAssociation rule is one of the significant and most used processes in the field of data mining andknowledge discovery. Further, the discovery of business intelligence software and techniquesassist in the data analysis and visualization while determining the underlying information. Inaddition to that, various association rules and algorithms are used for in the business intelligencesoftware and procedure for evaluating the relation between different elements in the database.This paper evaluates the various processes and conducts a survey of five articles related toassociation rule. The survey has provided detailed information regarding the different techniquesand processes employed for the association rule and business intelligence.
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2 ASSOCIATION RULETable of Contents1. Introduction..................................................................................................................................32. Survey..........................................................................................................................................42.1. Introduction...............................................................................................................................42.2. Literature Survey......................................................................................................................52.2.1. Article 1: Association Rule Approach for Evaluation of Business Intelligence forEnterprise Systems...........................................................................................................................52.2.2. Article 2: Association Rules Mining for Business Intelligence.............................................72.2.3. Article 3:Comparative Survey on Association Rule Mining Algorithms.............................92.2.4. Article 4: Performance Analysis of Apriori and FP-Growth Algorithms (Association RuleMining)..........................................................................................................................................102.2.5. Article 5: Association Rule Mining with Apriori and FP - growth Using Weka................122.3. Critical Review/ Analysis.......................................................................................................133. Summary...................................................................................................................................14Reference.......................................................................................................................................15
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3 ASSOCIATION RULE1. Introduction The theory of business intelligence (BI) is often referred to as a combination of variouspractices, application and technologies bused for collecting, integrating, analyzing andpresenting new information. Zhao and Bhowmick (2015, p.64) showed that the application ofvarious BI process allows any business organization to analyze, store and access data for thedecision making process. According to Moro, Cortez and Rita (2015, p.1322), content and datamust not be characterized as separate object, but need to me utilized together in a integratedformat for assisting generating more business opportunities. In order to keep track, analyze andmonitor the significant data, organizations used various technologies and software applications.Kasemsap (2015, p.25), showed that development of the BI software has been developed withthe significant goal for importing, extracting and analyzing the data for revealing the insight ofbusiness information. Larose (2014, p.6), claimed that in today’s world with the advancement ofinformation technology, data are being produced in significant velocity and volume. Theexponential increment of data has made it difficult for the business organization to select andidentify the crucial information for assisting in business decision making process. Fan, Lau andZhao (2015, p.91), showed that data mining procedures, processes and techniques are beingwidely used in both scientific and commercial domain for analyzing and extracting huge amountof data that are both in structured and unstructured or mixed formation. Over the past decade,various tools, techniques and algorithm have been proposed and developed for mininginformation for the process of BI. Giannotti et al., (2013, p. 388) claimed that various softwareand applications used for the Business Intelligence often employ the techniques and theories ofdata mining and association rules.
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