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CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE EVENT_CODE JAN2016 ASSESSMENT_CODE MIT401_JAN2016 QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 11567 QUESTION_TEXT List the objectives of data mining in telecommunication. SCHEME OF EVALUATION 1.Helps to understand the business involved, identify telecommunication patterns, catch fraudulent activities, make better use of resources and improve the quality of service. 2.Algorithms include CART, c4.5, neural networks and Bayesian classifiers among others. 3.The ability to handle noise in this case is obviously critical to the successful application of data mining algorithms. 4.The company’s face the problem of churning. 5.Data mining is one solution to do appropriate credit scoring and to combat churns in the telecom industry. 6.Used to churn analysis to perform 2 key tasks: Predict and Understand. 7.Decision support in telecommunication forms the rules that can be used as decision support rules. 8.In central system RTKP procedure based on conjunctive and disjunctive matrices and operators. 9.KDD has delivered a variety technique to discover patterns from vast amount of data which helps in mining for complex data. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 11568 QUESTION_TEXT List the points which describe the process of Knowledge Discovery. SCHEME OF EVALUATION There are 9 points. Each carries 1 Marks. If all points explained then 10 marks to be given. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 11572 QUESTION_TEXT Explain various characteristics of data warehouse? 1.Subject oriented 2.Integrated SCHEME OF EVALUATION 3.Non Volatile 4.Time variant (2.5 marks each)(10 marks) QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 126112 QUESTION_TEXT Explain briefly a. Hierarchical clustering b. Divisive clustering SCHEME OF EVALUATION a. b. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 126114 Hierarchical clustering (5 marks) Divisive clustering (5 marks) Discuss the following data warehouse schema a. Star schema QUESTION_TEXT b. Snowflake schema a. Star schema (5 marks) b. Snowflake schema (5 marks) SCHEME OF EVALUATION QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 126115 Explain the categories of web mining. QUESTION_TEXT SCHEME OF EVALUATION Web mining can be broadly divided into three catagories. a)Web content mining. b)Web structure mining. c)Web usage mining. a)Web content mining : web content mining targets the knowledge discovery in which the main objects are the traditional collections of multimedia document such as images , video and audio which are embedded in or linked to the web pages. Web content mining could be differentiated from two points of view: Agent based approach or database approach. The first approach aims on improving the information finding and filtering. The second approach aims on modeling the data on web into more structured form in order to apply standard database querying mechanism and datamining application analyze it. Web content mining problems and challenges are data/information extraction , web information integration, opinion extraction from online sources,knowledge synthesis, segmenting web pages and detecting noise . b) Web structure mining: this focuses on analysis of the link structure of the web and one of its purpose is to identify more preferable documents. The different objets are linked in some way. The appropriate handling f the links could lead to potential correlations and then improve the predictive accuracy of the learned models. The goal of the wb structure mining is to generate structural summary about the web site and web page. Based on the topology web structure mining will categorize the web pages and generate the information such as the similarity and relationship between different web sites. Web structure mining can also have another direction discovering the structure of web document itself. This type of structure mining can be used to reveal the structure of web pages. c) Web usage mining: this focuses on the techniques that could predict the behavior of users while they are interacting with the WWW. Web usage mining discover user navigation patterns from web data tries to discover the use full information from the secondary data derived from the interactions of the users while surfing on the web. Web usage mining collects the data from web log records to discover user access patterns of web pages .The insight knowledge could be utilized in personalization, system improvement, site modification, business intelligence and usage characterization. In general there are mainly 4 kinds of data mining techniques applied to the web mining domain to discover the user navigation pattern: Association rue mining, sequential pattern mining, clustering, classification.
