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"Sharpening Skills..... Serving Nation" International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459 (Online), Volume 4, Special Issue 1, February 2014) International Conference on Advanced Developments in Engineering and Technology (ICADET-14), INDIA. Data Mining to support Decision Process in Decision Support System Keshav Jindal1, Manoj Sharma2, Dr. B. K Sharma3 1,2 Assistant Professor, 3Principal Scientific Officer, NITRA Technical Campus, Ghaziabad [email protected], [email protected], [email protected] Abstract-- Recent alliance use a come to of types of decision support systems to make feasible decision support. In lots of gear OLAP based tools are used in the industry area enabling quite a few views on data and all the way on or after side to side that a deductive go forward to data analysis. Data mining make bigger the credible for decision support by discover pattern and relatives unseen in data and therefore enabling an inductive stir towards to data analysis. The use of data mining to construct possible decision support enables novel approaches to difficulty solving. The paper introduces our approach to adding of decision support systems with data mining methods. We commence a data mining based decision support system designed for creation users enabling them to use cluster rule models to make easy decision support by means of barely a essential level of acquaintance of data mining. Keywords-- data mining, decision support system, decision support, alliance rules I. INTRODUCTION Modern organization use several types of decision support systems to make easy decision support. For the rationale of investigation and decision support in the business area in a lot of belongings OLAP based decision support systems are worn [2]. Performing scrutiny from side to side OLAP follows a deductive approach of analyzing statistics. The disadvantage of such an move towards is that it depends on chance or even luck of choosing the accurate dimensions at drilling-down to get hold of the most expensive in sequence, trends and patterns. We possibly will say that OLAP systems make available systematic tools enabling user-led scrutiny of the data, where the user has to get going the right uncertainty in order to get the suitable answer [1]. Such an approach enables above all the answers to the questions like: “What is overall profits for the first neighbourhood grouped by customers?” What about the answers to the questions like: “What are characteristics of our best customers?” Those answer cannot be provided by OLAP systems, but by the using data mining. Amateur dramatics analysis through data mining follow an inductive move towards of analyzing data. Data mining is a development of analyzing data in command to determine implicit, but potentially useful in turn and uncover up to that time unknown patterns and associations hidden in data. The use of data mining to smooth the progress of decision support can show the way to an improved performance of decision making and can facilitate the tackle of new types of sweat that have not been addressed before. The amalgamation of data mining and decision support can appreciably improve current approach and create new approach to problem solving, by enable the synthesis of knowledge from experts and knowledge extract from data [1]. The paper introduces decision support system called DMDSS (Data Mining Decision Support System) which is based on data mining. DMDSS enables integration of data mining keen on decision processes by enabling repetitive creation of data mining models. In DMDSS, data mining models be created by data mining experts and browbeaten by industry users. II. D ATA M INING Data mining, or knowledge unearthing, is the computer-assisted practice of digging from beginning to end and analyzing mammoth sets of data and then extracting the meaning of the data. Data mining rigging envisage behaviours and future trend, allowing businesses to formulate down to business, knowledgedriven decisions. Data mining tools can come back with business questions with the intention of conventionally were besides time consuming to resolve. They comb databases for hidden patterns, finding predictive in sequence that experts may miss since it lies external their potential. Data mining derive its name from the similarities between penetrating for steep information in a large record and withdrawal a mountain for a stratum of valuable ore. Both processes necessitate either sifting from side to side an immense amount of material, or astutely penetrating it to find where the value resides. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 41 "Sharpening Skills..... Serving Nation" International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459 (Online), Volume 4, Special Issue 1, February 2014) International Conference on Advanced Developments in Engineering and Technology (ICADET-14), INDIA. Even though data mining is still in its infancy, company in a wide range of industries - including put on the market, finance, wellbeing care, industrialized shipping, and aerospace - are already using data mining apparatus and techniques to take advantage of chronological data. By means of pattern gratitude technologies and statistical and mathematical techniques to sift through warehoused in sequence, data mining helps analysts recognize noteworthy facts, relationships, trends, patterns, exceptions and anomalies that might or else go unobserved. For businesses, data mining is used to determine patterns and relationships in the statistics in order to help make better business decisions. Data mining can help smudge sales trends, develop smarter marketing campaigns, and accurately envisage customer fidelity. Specific uses of data mining embrace: Market segmentation - Identify the widespread description of patrons who buy the identical products commencing your theatre company. Customer shake - envisage which clientele are likely to leave your troupe and go to a contestant. Fraud recognition - Identify which dealings are most likely to be deceitful. Direct advertising - Identify which prediction should be included in a mailing list to attain the highest rejoinder rate. Interactive marketing - envisage what each individual accessing a Web site is most likely interested in considering. Market basket investigation - Comprehend what products or armed forces are commonly purchased together; e.g., beer and diapers. Drift scrutiny - Reveal the dissimilarity between typical customers this month and preceding. III. INTEGRATING D ATA M INING AND DECISION SUPPORT Companies use quite a lot of types of decision support systems to smooth the progress of decision support. For the purposes of investigation and decision support in the dealing area habitually OLAP based decision support systems are worn. OLAP systems correspond to a tool enabling decision support on a deliberate level. They enable drill-down concept, i.e. digging through a data warehouse on or after several viewpoints to acquire the information the decision architect is interested in [2]. OLAP systems support investigation processes and decision processes, where the analysts are supposed to look for information, trends and patterns. They do it by performance OLAP forms substitution dimensions and drilling-down from beginning to end them [4]. Performing analysis from first to last OLAP follows a deductive come near of analyzing data [10]. The disadvantage of such an come within reach of is that it depends on twist of fate or even luck of choosing the accurate dimensions at drilling-down to acquire the most valuable information, trends and patterns. We could say that OLAP systems provide analytical tools enabling user-led analysis of the data, where the user has to start the right query in order to get the appropriate answer [5]. Such an approach enables mostly the answers to the questions like, “what are best characteristics of our best customer” those answer cannot be provided by OLAP systems, but can be provided by the use of data mining, which follows an inductive approach of analyzing data [10]. When discussing the relation between data mining and OLAP it is not the question of which one of them is better or worse. Data mining enables the answers to different questions than OLAP, i.e. it enables the solution of different problems and to acquire different information. Decision processes in general, depending on problem, need both, OLAP and data mining, to get the appropriate level of support of decision processes [6]. Several authors discuss the use of data mining to facilitate decision support and they all confirm the value of it. Chen and Liu argue that the use of data mining helps institutions make critical decisions faster and with a greater degree of confidence. They believe that the use of data mining lowers the uncertainty in decision process [7]. Nemati and Barko state that the use of data mining offers companies an indispensable decision-enhancing process to exploit new opportunities by transforming data into valuable knowledge and a potential competitive advantage. Authors also introduce their survey which indicates that the use of data mining can improve the quality and accuracy of decisions [8]. Lee and Park state that the knowledge gained from data sources by the use of data mining methods can be crucial for the decision making processes. Mladenic claims that the integration of data mining and decision support can lead to the improved performance of decision support systems and can enable the tackling of new types of problems that have not been addressed before. They also argue that the integration of data mining and decision support can significantly improve current approaches and create new approaches to problem solving, by enabling the fusion of knowledge from experts and knowledge extracted from data. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 42 "Sharpening Skills..... Serving Nation" International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459 (Online), Volume 4, Special Issue 1, February 2014) International Conference on Advanced Developments in Engineering and Technology (ICADET-14), INDIA. 3.1. Data Mining Software Tool Approach Data mining can be used through two different approaches. The first approach is called data mining software tool approach where the use of data mining is typically initiated through ad hoc data mining projects [4, 9]. Ad hoc data mining projects are initiated by a particular objective on a chosen area which represents a basis for the defining of the domain. They are performed using data mining software tools which require a significant expertise in data mining methods, databases and/or statistics. They usually operate separately from the data source, requiring a significant amount of additional time spent with data export from various sources, data import, pre-processing, post-processing and data transformation. The result of a project is usually a report explaining the models acquired during the project using various data mining methods. Data mining software tool approach has a disadvantage in a number of various experts needed to collaborate in a project and in transferability of results and models [11]. The latter indicates that results and models acquired by the project can be used for reporting, but cannot be directly utilized in other application systems. Data mining software tool approach represents the first generation of data mining. 3.2. Data Mining Application System Approach The data mining software tool approach has revealed some disadvantages. The most important of them is the fact that due to the complexity of data mining software tools, they can not be directly used by business users. Data mining models are produced for business users. For that reason we need applications which will enable them to view and exploit data mining models effectively to facilitate decision support. This implies to the new approach of the use of data mining which we call data mining application system approach. It is an approach which focuses on business users and other decision makers, enabling them to view and exploit data mining models. Models are presented in a user-understandable manner through a user friendly and intuitive GUI using standard and graphical presentation techniques. Decision makers can focus on specific business problems covered by areas of analysis with the possibility of repeated analysis in periodic time intervals or at particular milestones. Through the use of data mining application system approach, data mining becomes better integrated in industry environments and their decision process. IV. INTRODUCTIONS O F DMDSS Our decision to expand DMDSS was also lying on the fact that the customary use of data mining from beginning to end data mining software apparatus does not bring data mining faster to business users for the reason that of involvedness of data mining tools. Data mining tools are very multifaceted and demand proficiency in data mining, i.e. considerate of data mining algorithms and parameters for algorithms. We sought after to develop a decision support system that would enable data mining experts to create data mining models and enable business users to take advantage of data mining models from beginning to end easy-to-use GUI. DMDSS was urbanized for a wireless network operator for the purposes of decision support in the area of analytical CRM (Customer Relationship Management). 4.1 A Process Model for DMDSS One of the key issues in the design of DMDSS was to settle on the data mining process model. The process representation for DMDSS is based on the CRISP-DM (Cross Industry Standard Process for Data Mining) process model. CRISP-DM process model breaks behind the data mining performance into the following six phases which all include a variety of tasks: business sympathetic, data understanding, data preparation, modelling, evaluation and deployment. CRISP-DM process model was modified to the needs of DMDSS as a two stage model: the training stage and the production stage. The division into two stages is based on the following two demands. First, DMDSS should enable frequent creation of data mining models based on an upto-date data set for every area of examination. Second, business users should only use it within the exploitation phase with only the basic level of indulgent of data mining concepts. Area of analysis is a business domain on which industry users perform analysis and construct decisions. The homework stage represents the process model for the use of DMDSS for the purposes of preparation of the area of scrutiny for the production use (Fig. 1). During the grounding stage, the CRISP-DM phases are performed in multiple iterations with the emphasize on the first five phases starting from business considerate and ending with appraisal. The aim of executing multiple iterations of all CRISP-DM phases for every area of analysis is to accomplish step-by-step improvements in several of the phases. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 43 "Sharpening Skills..... Serving Nation" International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459 (Online), Volume 4, Special Issue 1, February 2014) International Conference on Advanced Developments in Engineering and Technology (ICADET-14), INDIA. In the business understanding phase, unimportant redefinitions of the objectives can be made, if necessary, according to the results of other phases, especially the results of the appraisal phase. In the data grounding phase the improvement in the procedures which implement recreation of the data set can be achieved. The data set must be recreated mechanically every night based on the current state of data warehouse and transactional databases. The trouble detected in the data preparation phase can also demand changes in the data sympathetic phase. In the modelling and appraisal phase, the representation is created and evaluated for several times to allow alteration of data mining algorithms through finding proper values of the algorithm parameters. It is indispensable to do enough iteration in order to monitor the level of changes in data sets and data mining models acquired and reach the firmness of the data grounding point and parameter values for data mining algorithms. Figure 1: Plan of the development form for DMDSS The mission of the preparation stage is to confirm the pleasing of the objectives of the area of analysis for decision support and to assure the firmness of data preparation. The construction stage represent the production use of DMDSS for the area of investigation (Fig. 1). In the production stage the importance is on the phases of modelling, assessment and deployment, which does not mean that other phase are not encompassed in the construction stage. Data preparation, for case in point, is executed automatically based on measures residential in the preparation stage. Modelling and appraisal are performed by a data mining professional, while the exploitation phase is performed by a production user. V. RELATED W ORKS Some decision support systems that use data mining have already been developed and introduced in the literature. We introduced a decision support system based on data mining. The system was designed to support tactical decisions of a basketball coach during a basketball match through suggesting tactical solutions based on the data of the past games. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 44 "Sharpening Skills..... Serving Nation" International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459 (Online), Volume 4, Special Issue 1, February 2014) International Conference on Advanced Developments in Engineering and Technology (ICADET-14), INDIA. The decision support system only supports the association rules data mining method and uses the association rule algorithm called Apriori algorithm combined with the Decision query algorithm. The decision support system enables the coach to submit data about his tactical strategies and data about the game and the rival team. After that the system provides the coach with opinion about the chosen strategies and with suggestions. The system is not designed to support other domains; it only supports the basketball domain. Bose and Sugumaran introduced the Intelligent Data Miner (IDM) decision support system [2]. IDM is a Web-based application system intended to provide organization-wide decision support capability for business users. Besides data mining it also supports some other function categories to enable decision support: data inquiry and multidimensional analysis through enabling OLAP views on multidimensional data. In the data mining part of IDM it supports the creation of models, manipulation of models and presentation of models in various presentation techniques of, among others, the following data mining methods: association rules, clustering and classifiers (classification). The system also performs data cleansing and data preparation and provides necessary parameters for data mining algorithms. An interesting characteristic of IDM is that it makes a connection to an external data mining software tool which performs data mining model creation. The system enables predefined and ad-hoc data mining model creation. The authors state that the disadvantage of IDM is the fact that nontechnical users (business users) need to have a fair amount of understanding of data mining and that the use of data mining and the creation of data mining models still needs to be clearly directed by the user, especially with ad-hoc model creation. Lee and Park presented the Customized Sampling Decision Support System (CSDSS) which uses data mining [12]. CSDSS is a web-based system that enables the user to select a process sampling method that is most suitable according to his needs at purchasing semiconductor products. The system enables the autonomous generation of the available customized sampling methods and provides the performance information for those methods. CSDSS uses clustering data mining method within the generation of sampling methods. The system is not designed to hold up other domain; it only ropes the domain mention. VI. CONCLUSIONS DMDSS has now been in making for several years. During the first year of its creation there will be supervise and consultancy provide by the expansion squad. The main goal of supervise and consultancy is to lend a hand the data mining commissioner in the concern. The person in charge for that role has an adequate amount of knowledge, because he was a constituent of progress team and all the time in attendance at grounding stage for every area of scrutiny. But, he has not an adequate amount of familiarity yet. Supervise will for the most part cover prop up at model appraisal and model explanation for data mining commissioner and selling users. Business users use DMDSS at their every day work. They use patterns and rules recognized in models as the original acquaintance, which they use for investigation and decision process at their work. It is fetching evident that they are accomplishment worn to DMDSS. According to their writing they have previously become attentive of the advantages of incessant use of data mining for investigation purposes. Based on the models acquired they have previously geared up some changes in promotion come near and they are planning a extraordinary customer group focused operation utilizing the acquaintance acquire in data mining models. The on the whole important attainment after several months of tradition is the fact that business users have really started to recognize the potentials of data mining. All of a swift they have got new ideas for new areas of analysis, because they have started to realize how to define area of investigation to acquire valuable grades. REFERENCES [1] [2] [3] [4] [5] D. Mladenic, N. Lavrac, M. Bohanec, S. Moyle, Data Mining and Decision Support: Integration and Collaboratio, Dordrecht, Kluwer Academic Publishers, 2002 R. Bose, V. Sugumaran, Application of Intelligent Agent Technology for Managerial Data Analysis and Mining, The DATABASE for Advances in Information Systems, 30, 1, pp. 7794. K.C. Chen, Decision Support System for Tourism Development: System Dynamic Approach, Journal of Computer Information Systems, 45, 1, pp. 104-112, 2004. M. Goebel and L. Gruenwald. A Survey of Data Mining Knowledge Discovery Software Tools. SIGKDD Explorations, Vol. 1, No. 1, 1999, pp. 20-33. D. Mladenic, N. Lavrac, M. Bohanec, S. Moyle, Data Mining and Decision Support: Integration and Collaboratio, (Chapter 1: Data Mining: Authors; N. Lavrac and M. Grobelnik), Kluwer Academic Publishers, Dordrecht, 2003, The Nederlands. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 45 "Sharpening Skills..... Serving Nation" International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459 (Online), Volume 4, Special Issue 1, February 2014) International Conference on Advanced Developments in Engineering and Technology (ICADET-14), INDIA. [6] G.A. Forgionne and B. Rudenstein Montano, B. Post Data Mining Analysis for Decision Support Through Econometrics. Information, Knowledge, Systems Management, Vol. 1, No. 2, 1999, pp. 145-157. [7] S.Y. Chen and X. Liu. The Contribution of Data Mining to Information Science. Journal of Information Science, Vol. 30, No. 6, 2004, pp. 550-558. [8] H. Nemati and C.D. Barko. Enhancing Enterprise Decisions Through Organizational Data Mining. Journal of Computer Information Systems, Vol. 42, No. 4, 2002, pp. 21- 28. [9] M. Holsheimer, M. Data Mining by Business Users: Integrating Data Mining in Business Process. Proceedings of International Conference on Knowledge Discovery and Data Mining KDD-99, 1999, pp. 266-291. [10] JSR-73 Expert Group. JavaTM pecification Request 73: JavaTM Data Mining (JDM), 2004, Java Community Process. [11] J. Srivastava, R. Cooley, M. Deshpande and P.N. Tan. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, Vol. 1, No. 2, 2000, pp. 12-23. [12] H.L. Lee, C. Park, Agent and Data Mining Based Decision Support System and its Adaption to a New Customer Centric Electronic Commerce, Expert Systems With Applications, 25, 4, pp. 619-635, 2003. AUTHOR’S PROFILE Keshav Jindal received the B.E. degree in Computer Sc. & Engg with Honours from Vaish College of Engineering, Rohtak. He obtained his M.Tech degree in Computer Engineering from P.D.M.C.E. He is working as Assistant Professor in Deptt of Computer Sc. & Engg, NITRA Technical Campus (Govt. Aided Self Finance), Ghaziabad. He has published more than 20 papers in national and international journals and conferences. Manoj Sharma received the B.E. degree in Computer Sc. & Engg with Honours from Vaish College of Engineering, Rohtak. He obtained his M.Tech degree in Software Engineering from U.I.E.T, M.D.U Rohtak. He is working as Assistant Professor in Deptt of Computer Sc. & Engg, NITRA Technical Campus (Govt. Aided Self Finance), Ghaziabad. He has published more than 06 papers in national and international journals and conferences. Dr. B.K. Sharma is M.Tech. & Ph.D. (Computer Science) from University of Rajasthan and also certified internal auditor course ISO-9000 from world-wide quality management network Ltd., London (UK). He has over 23 years of experience in academic, Industry and research. Currently Dr. Sharma is Principal Scientific Officer & Head Software Development Centre, NITRA, Ghaziabad. Northern India Textile Research Association (NITRA) one of the four textile research association and linked to the Ministry of Textile, Govt. of India. Lord Krishna College of Engineering (An ISO 9001:2008 Certified Institute) Ghaziabad, Uttar Pradesh, INDIA. Page 46