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CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE EVENT_CODE APR2016 ASSESSMENT_CODE MCA5043_APR2016 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 11571 QUESTION_TEXT What are the objectives of using data mining in business? Explain. SCHEME OF EVALUATION There are 8 objectives. Each carries 1.25 Marks QUESTION_T DESCRIPTIVE_QUESTION YPE QUESTION_ID 72814 QUESTION_T Define Data Mining. Differentiate between Data Mining and DBMS. EXT Data Mining: It is the search for the relationships and global patterns that exist in large databases but are hidden among vast amounts of data, such as relationship between patient data and their medical diagnosis. It is the process of discovering meaningful, new correlation patterns and trends by sifting through large amounts of stored in repositories, using pattern recognition techniques. (2 marks) DBMS VS Data Mining SCHEME OF EVALUATION (8 marks) QUESTION_T DESCRIPTIVE_QUESTION YPE QUESTION_I 126111 D QUESTION_T Explain the basic tasks involved in Data transformation. EXT Selection : This takes place at the beginning of the whole process of data transformation. You select either the whole records or parts of several SCHEME OF records from the source systems. The task of selection usually forms part EVALUATIO of the extraction function itself –2 Marks N Splitting/Joining : This task includes the types of data manipulation you need to perform on the selected parts of source records. Sometimes you will be splitting the selected parts even further during data transformation. Joining of parts selected from many source systems is more widespread in the Data Warehouse environment 2 Marks Conversion : This is an all–inclusive task. It includes a large variety of rudimentary conversions of single fields for two primary reasons – one to standardize among the data extraction from disparate source systems, and the other to make the fields usable and understandable to the users 2 Marks Summarization : Sometimes you may find that it is not feasible to keep data at the lowest level of detail in your Data Warehouse. It may be that none of your users ever need data at the lowest granularity for analysis or querying 2 Marks Enrichment : This task is the rearrangement and simplification of individual fields to make them more useful for the Data Warehouse environment. You may use one or more fields from the same input record to create a better view of the data for the Data Warehouse. This principle is extended when one or more fields originate from multiple records, resulting in a single field for the Data Warehouse 2 Marks QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 126113 QUESTION_TEXT Briefly explain any TWO scientific applications using data mining SCHEME OF EVALUATION a. b. c. Biomedical engineering Telecommunications Climate data and the earth’s ecosystems (Any 2 from the above which carries 5 marks each) QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 126114 QUESTION_TEXT Discuss the following data warehouse schema a. Star schema b. SCHEME OF EVALUATION a. Snowflake schema Star schema (5 marks) b. Snowflake schema (5 marks)