Association Rule in Business Intelligence †MyAssignmenthelp.com

Question: Discuss about the Association Rule in Business Intelligence. Answer: Introduction The theory of business intelligence (BI) is often referred to as a combination of various practices, application and technologies bused for collecting, integrating, analyzing and presenting new information. Zhao and Bhowmick (2015, p.64) showed that the application of various BI process allows any business organization to analyze, store and access data for the decision making process. According to Moro, Cortez and Rita (2015, p.1322), content and data must not be characterized as separate object, but need to me utilized together in a integrated format for assisting generating more business opportunities. In order to keep track, analyze and monitor the significant data, organizations used various technologies and software applications. Kasemsap (2015, p.25), showed that development of the BI software has been developed with the significant goal for importing, extracting and analyzing the data for revealing the insight of business information. Larose (2014, p.6), claimed that in todays world with the advancement of information technology, data are being produced in significant velocity and volume. The exponential increment of data has made it difficult for the business organization to select and identify the crucial information for assisting in business decision making process. Fan, Lau and Zhao (2015, p.91), showed that data mining procedures, processes and techniques are being widely used in both scientific and commercial domain for analyzing and extracting huge amount of 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 mining information for the process of BI. Giannotti et al., (2013, p. 388) claimed that various software and applications used for the Business Intelligence often employ the techniques and theories of data mining and association rules. This particular report aims at evaluating the role of data mining and association rule in business intelligence. Apart from that, the reports tend to evaluate the various process and algorithms of association rules are widely used for data mining. Furthermore, based on the information gained, the study will try to identify the better process or algorithm that would enhance the business intelligence process with association rule. Slimani and Lazzez (2014, p. 143) showed that the techniques of association rules are widely used in the business operations for the discovery of the various products and services combinations that the customers tends to purchase together. Further, the application of the association rules ensures the determination and evaluation of the underlying relationship between the various products and the hierarchy of products used for purchase of products. This particular chapter aims at evaluating and reviewing five past articles for evaluating the use of association rules in business intelligence and various processes of techniques used for data mining in business organizations. Association Rule Approach for Evaluation of Business Intelligence for Enterprise Systems Paper is About: In this particular article, the author showed that data mining processes and techniques are extensively used in the business intelligence software for identifying crucial information related to business operations. In this article, the authors have highlighted the significant application of association rule algorithms for data mining process in business organizations. Furthermore, Rouhani et al., (2012, p.8) have provided a process map using the association rules algorithm for business intelligence. In addition to that, the article has represented a combined process used with the apriori and business intelligence techniques used for data evaluation and analysis in business organization. Apart from that traditional process of using association rule in business intelligence for extracting information has also been reviewed. Technical Details: Rouhani et al., (2012, p.2) have defined the role of the association rule in data mining is for detecting the underlying association or relation in large volume of data set based on defined nominal attribute values. Further it has been identified that the association rule is used for discovering the relation between different items used from the huge set of transaction. Further, the author has used Apriori algorithm as example for of association rule in business processes. Rouhani et al., (2012, p.4) have further showed that association rule is widely used for analyzing market analysis for evaluating the pattern of store types, mail order and purchase of the items in the supermarket. Confidence and support are the two major variable used in association rules for determining the interestingness of the association rule applied for data mining. Rouhani et al., (2012, p.1) claimed that the association rule is considered if the evaluated level of confidence and support are greater that pre-defined confidence and support respectively. Used in association rule in BI: Rouhani et al., (2012, p.5) have stated that in current business operations and environments, ERP (Enterprise Resource Planning) systems are widely used for conducting major operations and computerizing the different procedure used by an organization. ERP systems are often associated with the revising various documents, filing forms and logging various transactions while registering the events that occur in the business and functional module of the organization. Rouhani et al., (2012, p.6) showed that he information and data stored and used in the ERP systems can be used for improving the efficiency and performance of the business processes. Furthermore it has been identified in the paper that process mining or business process mining is utilized for explaining and analyzing the behavior of various process and logs. Therefore, the application of various data mining tools, processes and algorithms would help in improving the data analysis and evaluation process. Further, Rouhani et al., (2012, p.4) showed that the process of data mining helps in process of knowledge discovery and decision making process. In addition to that, the increased use of data mining application and tools are used for providing tremendous support for improving the quantity measurement in business operations. Association Rules Mining for Business Intelligence Paper is About: This particular paper evaluated the process and techniques used in business intelligence and data mining. The author has identified a relation between the data mining and business intelligence used in business operations (Jha, 2014). The paper identified the essential stages and phases of business while determining the needs of data analysis and evaluation for the decision making procedure of organization. Furthermore, the paper has aimed at determining the various challenges and requirement of data mining followed during the business intelligence process (Jha, 2014). Apart from that, the author has classified the various techniques used in the process of data mining while reviewing and analyzing the association rules and algorithms that are being used in the business process. Technical Details: In this paper, data mining has been defined as an issue dependent on the application. Further, Jha, (2014) claimed that various application required different data mining techniques and rule for evaluating the detailed information. Jha, (2014) showed that mining association rule is the major rule that has been widely used by various applications. Two vital algorithm have been identified in the article, namely Nave algorithm and Apriori Algorithm. Further, the author has presented the association of two methods used in the transaction of the items and determined the support and predictability of the association rules obtained from both the algorithm. Used in association rule in BI: Jha, (2014) illustrated that huge data are being generated by various electronic devices and application of barcodes in the departmental and business has increased the amount and velocity of data generation. Further, the author has provided example by stating and on a daily basis, Wal-Mart generated 20 million data based on transaction of good. The application of the association rule for the mining of information assist the analysis in gaining information that makes sense to the business processes (Jha, 2014). The application of the association rule allows the determining and identifying the habit of the customer in purchasing items from the store. Apart from basket analysis, association rule has significant application in classification, e-commerce, finances, web mining, customer segmentation and marketing. Comparative Survey on Association Rule Mining Algorithms Paper is About: Girotra et al., (2013, p.20) have identified the popularity of association rule in the process of data mining among the business organization and marketers. The author has showed that association rule in data mining and discovery creates various subset of the large item set for determining the existing relationship between the items. In this paper, the author has identified that the association rule is divided into two significant parts. The initial phase determines the most frequent sets used among the large volume of unstructured or structured data (Girotra et al., 2013, p.18). While the second phase is used to determine the association rule or relationship between them. The paper has evaluated the different algorithm and process that are used for the association rule identification in knowledge discovery. Technical Details: The authors have identified eight significant algorithms that are frequently used in the association rules. The algorithm identified includes FP- Growth, Recursive Elimination, Eclat, Apriori hybrid, AprioriTID, Apriori, SETM and AIS. Various characteristic and functions of the different algorithms has been identified in the article. In addition to that, the authors have developed a detailed comparison of the various algorithms based on their features and functions (Girotra et al., 2013, p.20). From the detailed comparison and analysis it has been identified that use of Apriori Algorithm is most useful in case of closed set of items while Eclat is most advantageous in case of free set of items. The author showed that Apriori Algorithm, showed more efficiency and advantageous in performance and operations considering the other entire algorithm identified. Furthermore, it has been illustrated that the technique and process of Recursive Elimination were better conside ring all the features of Apriori but poorer when compared to Eclat algorithm used. Used in association rule in BI: The theory of association rule has been developed for identifying the most frequent item set purchased and often considered as the market basket analysis pattern or process. Girotra et al., (2013, p.21) claimed that the business executives and the professionals often uses the theory and technique of association rue for developing the layout plan and to place the services and items placed together for increasing the purchase rate and efficiency. Furthermore, the author claimed in todays business world, where technology and hand held devices are widely used, data are generated at high rate. The detailed information related to business are essential for developing various strategies related to business analysis (Girotra et al., 2013, p.22). The application of business intelligence helps in extracting raw information and provides structure understandable data that are useful for decision making process in business. Therefore, the application of association rule and data mining technique would help the analyst in performing the data evaluation in easier and much efficient manner. Performance Analysis of Apriori and FP-Growth Algorithms (Association Rule Mining) Paper is About: In this paper, the author has identified that association rule and data mining techniques are widely used in various data analysis and mining process all over various industries (Bala et al., 2016, p.283). The selected paper aims at defining the association rule and the concept of data mining used for data analysis and knowledge discovery in various business organizations. The author has identified the two significant algorithm of association rule namely FP Growth and Apriori for comparison and analysis of the difference of performance on the data identification and analysis (Bala et al., 2016, p.291). In addition to that, the author has used WEKA software for determining and evaluating the effectiveness of the performance of the association rule. Technical Details: While analyzing the apriori algorithm, the author has identified the procedure and techniques used for determining the association rule. The author has showed apriori runs on four significant steps. The initial step includes the generation of the element dataset of most frequently used items (Bala et al., 2016, p.286). The step is followed by join step process for identifying the number of candidate in the frequent step and self join with each other. The necxt step is followed by the pruning of the frequent dataset. In the last step, the data set that are least used are pruned from the association rule. Bala et al., (2016, p.283) showed that application of apriori algorithm uses more space results in the increased database. Similarly, FP Growth analysis has been analyzed for the evaluation of the performance. It has been identified that FP growth uses two phase process, first for developing the dataset and second phase for traversal of the FP tree. Used in association rule in BI: the association rule in the process of business intelligence can be utilized for digging out the relevant information regarding various process and technique used in business processes. The author has demonstrated that the applications of association rule in the business intelligence process allows in extracting hidden and underlying information from huge dataset and network for determining the potential association between the neglected items and predict the trends that can be used in decision making procedure (Bala et al., 2016, p.289). Association Rule Mining with Apriori and FP - growth Using Weka Paper is About: Mishra, Pani and Ratha (2015, p.2841) have showed association rule is one of the significant technique for mining significant information. In this paper, the author has determined and evaluated the significant technique and process used for the two major association rule including Apriori and FP Growth (Mishra, Pani and Ratha 2015, p.2838). The paper determines the effectiveness and performance of the two major algorithms. The author has used WEKA tools for determining the efficiency of the algorithm. Based on the analyzing and association rule generation in WEKA application, Technical Details: The paper showed various clustering technique used in the association rule including Model-based methods, Grid-based methods, Density based methods, Hierarchical Agglomerative and Partitioning Methods. Mishra, Pani and Ratha (2015, p.2840) author showed that Apriori Algorithm uses large dataset that are kept in lexicographic order. Furthermore, it has been identified that in Apriori method, the item set are detected on the basis of various pass and transaction while eliminating the item assets that are less frequently used. On the other hand, FP Growth algorithm is characterized with prefix tree structure, fragment pattern growth and frequent pattern for developing the FP tree. Used in association rule in BI: The author has claimed that association rule is one of the revolutionary technology and procedure used for determining the data evaluation and pattern. The process of association rule in data mining allows in determining the correlation between the different patterns in huge volume of relational database. Therefore, the author claimed that the association rule are used in the business intelligence on data warehouse for managing the multidimensional data and accessing information for professional use and decision making procedure of the business organization (Mishra, Pani and Ratha 2015, p.2839). The data mining technique provides visualization, analysis, recognition and warehousing of huge information for enhancing the performance and efficiency. Critical Review/ Analysis The above five journal articles have demonstrated and evaluated the various algorithms and processes used in the data mining and association rule for identifying the underlying relation between huge dataset Zhao and Bhowmick (2015, p.64). The detailed literature survey of the various papers has showed that association algorithm and apriori algorithm are widely used in various process and techniques. The literature survey has demonstrated the significant and performance analysis of the various association algorithm and observed that Apriori algorithm has maximum amount of performance efficiency when considered between various algorithms with closed dataset. Further, it has been identified that all the authors have included the application of Apriori Algorithm in their analysis and evaluation process Moro, Cortez and Rita (2015, p.1322). The application of the Apriori algorithm can be considered standard for data mining and evaluation for large dataset. In addition to that, from the da ta evaluation of the survey, it has been observed that authors tends to use WEKA software tools for evaluation and determining the efficiency of the algorithm while developing the association rule between the algorithm. In addition to that, the application of association algorithm for data mining, evaluation and visualization provides detailed information about the underlying process and items that can be used for the decision making process of the business. Therefore, it can be claimed that association rule for data mining is a subset of the business intelligence process used in various industries. Furthermore, Slimani and Lazzez (2014, p. 143) claimed that based on the needs and requirement of the business intelligence software and the observation, various algorithm are employed for determining the existing and underlying trends and pattern in the information. The above survey of past literature has able to provide detailed information about the various association algorithm used in the data mining process. Various business intelligence software and systems including ERP, CRM and CSM used data mining algorithms, tools and techniques for taking business intelligence. The discovery of association rule and algorithm have increased and improved the efficiency of the knowledge discovery in business intelligence process. The detailed survey focused on the application of association rules and techniques for data mining process. The survey was able to provide detailed information regarding the different procedure and need of association rule in the business intelligence. The detailed survey showed that the apriori algorithm provides efficient result in the process mining and evaluation of detailed information. Therefore, the above evaluation was successful in determining the significant process, technique and procedure that are frequently used in association algorithm. In addition to that, the survey has focused and showed light in the application of association rule for the data intelligence and fact finding technique used in the business intelligence procedure of various business organizations. Reference Rouhani, S., Ghazanfari, M., Jafari, M., Akhavan, P. (2012). Association Rule Approach for Evaluation of Business Intelligence for Enterprise Systems.The IUP Journal Of Computer Sciences,V, No. 2, 1-19. Retrieved from https://www.researchgate.net/publication/228133328 Jha, R. (2014). Association Rules Mining for Business Intelligence.International Journal Of Scientific And Research Publications,4(5), 1-5. Retrieved from https://www.ijsrp.org/ Girotra, M., Nagpal, K., Minocha, S., Sharma, N. 2013. Comparative Survey on Association Rule Mining Algorithms.International Journal Of Computer Applications (0975 8887),84(10), 18-22. Retrieved from https://www.ijcaonline.org/ Reference: Bala, A., Shuaibu, M., Lawal, Z., Zakari, R. 2016. Performance Analysis of Apriori and FP-Growth Algorithms (Association Rule Mining).Alhassan Bala Et Al, Int.J.Computer Technology Applications,7 (2), 279-293. Retrieved from https://www.ijcta.com Mishra, A., Pani, D., Ratha, D. 2015. 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Association Rules Mining for Business Intelligence.International Journal Of Scientific And Research Publications,4(5), 1-5. Retrieved from https://www.ijsrp.org/ Kasemsap, K. 2015. The role of data mining for business intelligence in knowledge management.Integration of data mining in business intelligence systems, 12-33. Larose, D. T. 2014.Discovering knowledge in data: an introduction to data mining. John Wiley Sons. Mishra, A., Pani, D., Ratha, D. 2015. Association Rule Mining with Apriori and FP - growth Using Weka, 2837 -2845. Moro, S., Cortez, P., Rita, P. 2015. Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation.Expert Systems with Applications,42(3), 1314-1324. Rouhani, S., Ghazanfari, M., Jafari, M., Akhavan, P. 2012. Association Rule Approach for Evaluation of Business Intelligence for Enterprise Systems.The IUP Journal Of Computer Sciences,V, No. 2, 1-19. Retrieved from https://www.researchgate.net/publication/228133328 Slimani, T., Lazzez, A. 2014. Efficient analysis of pattern and association rule mining approaches.arXiv preprint arXiv:1402.2892. Zhao, Y., Bhowmick, S. S. 2015. Association Rule Mining with R.A Survey Nanyang Technological University, Singapore.