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    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME 74 DATA MINING APPROACH FOR GENERATION OF OPTIMAL INTERNET TELECOM PLANS Harshita Kotian1 , Divya Bhat2 , Charvi Kunder3 , Prof. Snehal Kulkarni4 1, 2, 3, 4 (Computer Engineering, St. Francis Institute of Technology/ Mumbai University, Mount Poinsur, S.V.P. Road, Borivali (West), Mumbai 400 103, India) ABSTRACT Decision support systems combine individual’s and computer’s capabilities to improve the quality of decisions. There are several decision support systems in Modern organizations to ease decision support. Data mining adds on the possibilities for decision support by discovering patterns and relationships hidden in data and therefore enabling the inductive approach of data analysis. One of its important applications is the price setting process or the calculation of expenditure and modifying plans based on user usage for various services provided to the customers by various firms. Telecom system is a system where information is very huge & continually updated. The main purpose of the paper is to represent an intelligent decision support system that will enable the service provider to gain vital insights into the prevailing trends and judge the upcoming profit and loss. The knowledgeable information thus got can be used to identify the plans that need to be modifying according to customer preference. Keywords: Decision Support Systems (DSS), Customer Relationship Management (CRM), Optimize, Online Analytical Processing (OLAP), Threshold, Telecom Industry (TI). 1. INTRODUCTION Decision support systems (DSS) are defined as interactive computer-based systems intended to help decision makers utilize data and models in order to identify problems, solve problems and make decisions. DSS provides support for decision making, they do not replace it. Modern organizations use several types of decision support systems to facilitate decision support. In many applications, tools based on OLAP are used in the business areas, which enable multiple views on data and through that a deductive approach to data analysis. Data mining INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME: www.iaeme.com/IJCET.asp Journal Impact Factor (2014): 8.5328 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME 75 increases the potential for decision support by discovering patterns and relationships hidden in data and therefore enabling the inductive approach of data analysis. One of the complex decision-making methods in a telecom company involves Tariff plan designing that require different types of information and knowledge. 1) Costs and how they alter with sales volume 2) Sales and how they vary with prices 3) Effect of competitor prices on the sales of the company’s plans In current scenario, there is a huge competition in the telecom industry between the service providers to provide best services to the customers in available possible least cost in order to attract new customers and also retain the older customers. In such scenarios, a telecom company has to make efficient decisions while declaring new plans keeping the customer transactions in mind. The customers buying patterns vary with time and also the data goes on increasing with time making the task of analyzing each and every customer’s transactions too complex. Here, comes a need for proposing a computerized system which while handle the task of scrutinizing the customer’s data more efficient [1]. Based on the above need, the paper aims at optimizing the internet tariff plans based on the profits made and customer preference. 2. RELATED WORK Modern organizations use several types of decision support systems to facilitate decision support. In many cases, tools based on OLAP are used in the business areas, which enable multiple views on data and through that a deductive approach to data analysis. Data mining adds the potential for decision support by discovering patterns and relationships hidden in data and therefore enabling the inductive approach of data analysis. DSS is user friendly software-based system proposed to help decision makers accumulate useful Information from a combination of underdone data, documents and individual knowledge or business models to recognize and resolve problems and make decisions. DSS can be used to represent all the information from any association in the form of charts, graphs i.e. in an abridged way, which helps the administration to take tactical verdict. The advantages of DSS are: • Increases personal efficiency • Fosters the speed of process of decision making • Makes up the organizational control • Encourages investigation • Speeds up problem solving in an association • Creates a aggressive advantage over competition • Enables finding out new approaches to thinking about the problem space • Helps automate managerial processes • produce pioneering ideas to speed up the performance. The use of data mining to facilitate decision support can lead to the improved performance of decision making and can enable the tackling of new types of problems that have not been addressed before. The paper [2] gives the concept of data mining based decision support system, which was intended for business users to enable them to use data mining models to make possible decision support with only a basic level of knowledge of data mining. We can improve current
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. approaches used and create new approaches to problem solving by amalgamating the idea of data mining and decision support system. The paper [3] assumes that the market is segmented into two customer classes i.e. as time sensitive customers and price sensitive customers. The customers who are ready to pay a premium price for a shorter service time are called as Time willing to accept a longer service time in return for a lower price are called as price sensitive customers. It is seen that the optimal profit in a time sensitive market is always larger than the optimal profit in a Price sensitive market. Thus a service provider will create more profit using market segmentation. Figure 1: Representation of problem statement [2] In this system, the in charge of the company can get a complete view of probed customer with the help of pie-charts, bar-charts and other forms of graphical representations. The system will work with dynamic data making it more efficient for the company in implementing decisions. The benefit is that the admin will get to know the percent different plans offered by the company. This will ensure the reasoning of less used plans and help them publicize more. This will enable the service provider to gain vital insights into the prevailing trends and judge the upcoming profit and loss. The knowledgeable information thus got can be used to identify the plans that need to be modifying according to customer preference. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 6375(Online), Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME 76 approaches used and create new approaches to problem solving by amalgamating the idea of data support system. The paper [3] assumes that the market is segmented into two customer classes i.e. as time sensitive customers and price sensitive customers. The customers who are ready to pay a premium price for a shorter service time are called as Time sensitive customers, while the customers who are willing to accept a longer service time in return for a lower price are called as price sensitive customers. It is seen that the optimal profit in a time sensitive market is always larger than the ofit in a Price sensitive market. Thus a service provider will create more profit using Representation of problem statement [2] In this system, the in charge of the company can get a complete view of probed customer charts and other forms of graphical representations. The system will work with dynamic data making it more efficient for the company in implementing decisions. The benefit is that the admin will get to know the percentage of contribution of revenue of the different plans offered by the company. This will ensure the reasoning of less used plans and help This will enable the service provider to gain vital insights into the prevailing the upcoming profit and loss. The knowledgeable information thus got can be used to identify the plans that need to be modifying according to customer preference. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), approaches used and create new approaches to problem solving by amalgamating the idea of data The paper [3] assumes that the market is segmented into two customer classes i.e. as time sensitive customers and price sensitive customers. The customers who are ready to pay a premium sensitive customers, while the customers who are willing to accept a longer service time in return for a lower price are called as price sensitive customers. It is seen that the optimal profit in a time sensitive market is always larger than the ofit in a Price sensitive market. Thus a service provider will create more profit using In this system, the in charge of the company can get a complete view of probed customer data charts and other forms of graphical representations. The system will work with dynamic data making it more efficient for the company in implementing decisions. age of contribution of revenue of the different plans offered by the company. This will ensure the reasoning of less used plans and help This will enable the service provider to gain vital insights into the prevailing the upcoming profit and loss. The knowledgeable information thus got can be used
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 3. BLOCK DIA /FLOW CHART OF DSS TI) Figure 2: A step-by-step process for obtaining The system will work with dynamic data making it more efficient for the company in implementing decisions. The customer data will be segregated into time data based on the plans which the customer has recharged to. Based on th sets of customers-Time Sensitive Customers and Price Sensitive Customers. On each of these two customer sets, a variant of FP Following these steps will lead to selection of a threshold value for classifying the plans to obtain optimal plans. After getting the likelihood of this data, the company can take the necessary steps in discarding or advertising or modifying these plans in order to mini Based on this data, new profitable plans can also be created with proper judgments. The plans which are less than threshold but are very close to it can be suggested to publicize the plans more so than more customers can avail than plan and its usage can be increased. The plans which are least used, i.e. which have very lesser value of plan count can be suggested to be discarded in the benefit of the company. The plans which are lesser than threshold and do not come in the a be modified in an efficient way by adjusting their parameters in such a way that customers will prefer it and start making use of it. Another benefit is that it will get to know the percentage of contribution of revenue of the different plans offered by the company. This will ensure the reasoning of less used plans and help them publicize more. 3.1. FREQUENT-PATTERN MINING ALGORITHM The FP-Growth Algorithm, proposed the complete set of frequent patterns structure for storing compressed and crucial information about frequent patterns named frequent pattern tree (FP-tree). This method outperforms e.g. Apriori Algorithm. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 6375(Online), Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME 77 BLOCK DIA /FLOW CHART OF DSS TI) step process for obtaining suggestions for optimal plans The system will work with dynamic data making it more efficient for the company in implementing decisions. The customer data will be segregated into time-sensitive and price sensitive data based on the plans which the customer has recharged to. Based on this the company will get two Time Sensitive Customers and Price Sensitive Customers. On each of these two customer sets, a variant of FP-Tree algorithm is applied as given below. Following these steps will lead to selection of a threshold value which be considered as a reference value for classifying the plans to obtain optimal plans. After getting the likelihood of this data, the company can take the necessary steps in discarding or advertising or modifying these plans in order to minimize risks and maximize profits. Based on this data, new profitable plans can also be created with proper judgments. The plans which are less than threshold but are very close to it can be suggested to publicize the plans more so than ail than plan and its usage can be increased. The plans which are least used, i.e. which have very lesser value of plan count can be suggested to be discarded in the benefit of the company. The plans which are lesser than threshold and do not come in the above two categories can be modified in an efficient way by adjusting their parameters in such a way that customers will Another benefit is that it will get to know the percentage of contribution of revenue of the erent plans offered by the company. This will ensure the reasoning of less used plans and help PATTERN MINING ALGORITHM proposed by Han, is an efficient and scalable method for mining patterns by pattern fragment growth, using an extended for storing compressed and crucial information about frequent patterns named frequent tree). This method outperforms other popular methods for mining frequent Customer Data Customer Segmentation (Time and Price) Apply Frequent Pattern Mining Algorithm on Time Sensitive Set Suggestion based on Speed, Validity, Cost and Capacity Apply Frequent Pattern Mining Algorithm on Price Sensitive Set Suggestion based on Speed, Cost and Capacity International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), suggestions for optimal plans The system will work with dynamic data making it more efficient for the company in sensitive and price sensitive is the company will get two Tree algorithm is applied as given below. value which be considered as a reference After getting the likelihood of this data, the company can take the necessary steps in mize risks and maximize profits. Based on this data, new profitable plans can also be created with proper judgments. The plans which are less than threshold but are very close to it can be suggested to publicize the plans more so than ail than plan and its usage can be increased. The plans which are least used, i.e. which have very lesser value of plan count can be suggested to be discarded in the benefit of the bove two categories can be modified in an efficient way by adjusting their parameters in such a way that customers will Another benefit is that it will get to know the percentage of contribution of revenue of the erent plans offered by the company. This will ensure the reasoning of less used plans and help by Han, is an efficient and scalable method for mining tended prefix-tree for storing compressed and crucial information about frequent patterns named frequent- for mining frequent patterns,
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME 78 FP tree Algorithm is used to find the usage of plans used by the customers. On applying the first half of the algorithm of finding the support of each item, we will obtain the actual support or count of each plan used by the customers. In the Second Step, a threshold value will be selected by the company in-charge which will decide the range of the optimal plan counts. Threshold value as referred above is the marginal value of the plan count. This value will serve as a line of separation between the most used plans and the least used plans of the company. The plan counts which are greater than and equal to the threshold value will be categorized into most used plans while the remaining plans which are lesser than threshold value will be categorized into least used plans. From here the company in-charge can get major insights regarding the popular plans and he can hence work on the less popular plans in order to maximize profits. 4. INPUT DATA SELECTION According to problem statement, there are many factors to be considered for giving best decision support system. The factors which we have considered are listed below, Current Customer Details The sample data set can be shown as, Figure 3: Details of all customers (Only few records shown)
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME 79 All the customer details including time sensitive and price sensitive customers are displayed. Basic customer details such as phone number, name, age gender and location has been displayed. Apart from this, it gives a clear idea of the plan the customer has opted for, how recently has the customer recharged, the number of times he has recharged and the total recharge amount paid by him/her, number of complains and plan change made, if any. Internet Plans Details Each internet plan is identified by the speed of the internet, its price premium, the validity of plan, i.e. whether yearly or half-yearly or monthly or on day basis. The validity of the plans will decide whether it is time-sensitive plan or price sensitive plan. Based on the plan which the customer recharges, he/she will be categorized into time-sensitive or price sensitive customer. The sample data set can be shown as, PID SPEED COST DURATION CAPACITY TYPE (TIME/PRICE SENSITIVE) 1 2 5999 1 year 25 GB Price 2 2 3999 1 year 15 GB Price 3 2 1499 3 months 5GB Price 4 2 2050 1 month 150 GB Time 5 2 1550 1 month 80 GB Time . . . . . . . . . . . . 11 2 99 1 month 350 MB Time 12 2 79 15 days 200 MB Time 13 2 56 7 days 150 MB Time 14 2 22 1 day 150 MB Time . . . . . . . . . . . . . . . . . . Figure 4: Details of all plans 5. EXPERIMENTAL RESULTS The results are displayed by using a certain customer dataset of a company which gave certain useful results in the benefit of the company. It was found out that about 82 % of the customers were time-sensitive while the remaining 18 % were price-sensitive customers as shown in Figure 5. Thus this analysis based on customer segmentation gave an idea of the kind of service that the customers prefer thus enabling them to take profitable decisions. A threshold percentage value was chosen. The admin has the flexibility to select the threshold. The plans have been further segmented into Time and Price sensitive plans.
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. Figure 5: Results showing analysis based on customer segmentation PID SPEED 4 2 6 2 7 2 8 2 10 2 11 2 12 2 14 2 1 2 Figure 6: Results showing the list of most used plans after a threshold of 50% was selected A threshold value is entered by the user, based on which all internet plans in the database are segmented into most used and least used plans. Plans Most used plans whereas plans below the threshold level are the least used plans. Above figure displays the result for Most used plans when the threshold value is inputted as 50%. Most used plans imply that most be profitable enough for the service provider. PID SPEED 5 2 9 2 Figure 7: Results showing suggestion for Time Sensitive Customers Price Sensitive Customers 0% 20% Analysis Based On Customer Segmentation International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 6375(Online), Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME 80 sults showing analysis based on customer segmentation COST DURATION CAPACITY COUNT 2050 1 month 150 GB 1050 1 month 30 GB 580 1 month 18 GB 499 1 month 12 GB 299 1 month 1.1 GB 99 1 month 350 MB 79 15 days 200 MB 22 1 day 150 MB 5999 1 year 25GB Results showing the list of most used plans after a threshold of 50% was selected A threshold value is entered by the user, based on which all internet plans in the database are segmented into most used and least used plans. Plans which are above the given threshold are the Most used plans whereas plans below the threshold level are the least used plans. the result for Most used plans when the threshold value is inputted as Most used plans imply that most of the customers prefer these plans and they are proving to be profitable enough for the service provider. COST DURATION CAPACITY COUNT 1550 1 month 80 GB 520 1 month 1.3 GB Results showing suggestion for modification of optimal plans for time sensitive customers 20% 40% 60% 80% 100% Analysis Based On Customer Segmentation Percentage Of Customers International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), sults showing analysis based on customer segmentation COUNT 12 12 14 11 16 9 12 13 15 Results showing the list of most used plans after a threshold of 50% was selected A threshold value is entered by the user, based on which all internet plans in the database are which are above the given threshold are the the result for Most used plans when the threshold value is inputted as of the customers prefer these plans and they are proving to COUNT 6 5 optimal plans for time sensitive customers Percentage Of Customers
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 74-81 © IAEME 81 PID SPEED COST DURATION CAPACITY COUNT 13 2 56 7 days 150 GB 3 Figure 8: Results showing suggestion for discarding of optimal plans for time sensitive customers Plan suggestions are provided for least used time sensitive plans and least used price sensitive plans. Three suggestions are provided for each of these two groups i.e. discard plans, modify plans and advertise plans as shown in Figure 7 and Figure 8. Plans closer to the threshold are advised to advertise to people and plans farthest to the threshold are most preferably advised to be discarded. The remaining plans in between the two have been suggested to be modified. Modifications could either be in the form of increasing or decreasing the speed, capacity or cost of the plans which would be decided by the company decision makers. 7. CONCLUSION Telecom companies are taking advantage of an opportunity to become a highly distinguished and recognized industry leader in the cellular communications industry by using the field of computerized decision support which is expanding to use new technologies and to create new applications. In order to achieve this goal, important achievement factors will be to recognize emerging trends and integrate them into operations, respond quickly to advancement in technology, provide high-quality services, and stay ahead of the technology curve by providing the best plans to its customers. The company should focus on 2 target markets that will provide with the greatest market penetration. The intention must be to offer service packages that are priced appropriately for each segment and will offer the services that best suit each segment's needs. 8. BIBLIOGRAPHY AND REFERENCES [1] Charvi Kunder, Divya Bhat and Harshita Kotian, “Decision Support System for Telecom Company”, International Journal of Computer Engineering & Technology, January 2014. [2] RokRupnik, MatjažKukar, Marko Bajec and MarjanKrisper, “DMDSS: Data Mining Based Decision Support System”, 28th International Conference on Information Technology Interfaces(ITI),2006 [3] Ki-sung Hong & Chulung Lee, “Integrated pricing and capacity decision for a telecommunication service provider”, Springer, 14 March 2012 [4] MortezaNamvar, [4] Mohammad R. Gholamian and SahandKhakAbi, “A Two Phase Clustering Method for Intelligent Customer Segmentation”, International Conference on Intelligent Systems, Modelling and Simulation, 2010. [5] Charvi Kunder, Divya Bhat and Harshita Kotian, “Decision Support System for Telecom Company”, International Journal of Computer Engineering & Technology (IJCET), Volume 5, Issue 1, 2014, pp. 103 - 111, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [6] A.Thirunavukarasu and Dr.S.Uma Maheswra, “Fuzzy Metagraph Based Knowledge Representation of Decision Support System”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 157 - 166, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.