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ASSIGNMENT
PROGRAM BSc
SEMESTER V
SUBJECT CODE & NAME Data Mining
CREDIT 4
BK ID BT9001
MAX.MARKS 60
Note: Answer all questions. Kindly note that answers for 10 marks questions should be approximately
of 400 words. Each question is followed by evaluation scheme.
Q.1 What is the relation between data mining and data warehousing? Explain.
Answer: - In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a
system used for reporting and data analysis. Integrating data from one or more disparate sources
creates a central repository of data, a data warehouse (DW). Data warehouses store current and
historical data and are used for creating trending reports for senior management reporting such as
annual and quarterly comparisons.
Q.2 Define data mining. State the differences between data mining and DBMS.
Answer:- Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD),an
interdisciplinary subfield of computer science, is the computational process of discovering patterns in
large data sets involving methods at the intersection of artificial intelligence, machine learning,
statistics, and database systems. The overall goal of the
Q. 3 Explain clustering and its applications.
Answer: - Application clustering (sometimes called software clustering) is a method of turning multiple
computer servers into a cluster (a group of servers that acts like a single system). Clustering software is
installed in each of the servers in the group. Each of the servers maintains the same information and
collectively they perform administrative tasks such as load balancing, determining node failures, and
assigning failover duty. The other clustering method, hardware clustering, requires that specialized
hardware be installed in a single server that controls the cluster.
5 How data mining is used in telecommunication field? Explain
Answer: - The telecommunications industry was one of the first to adopt data mining technology. This is
most likely because telecommunication companies routinely generate and store enormous amounts of
high-quality data, have a very large customer base, and operate in a rapidly changing and highly
competitive environment. Telecommunication companies utilize data mining to improve their marketing
efforts, identify fraud, and better manage their telecommunication networks. However, these
companies also face a number of data mining challenges
Q.6 Draw and explain the architecture of data mining based IDS and explain.
Answer: - Data mining is the process of discovering actionable information from large sets of data. Data
mining uses mathematical analysis to derive patterns and trends that exist in data. Typically, these
patterns cannot be discovered by traditional data exploration because the relationships are too complex
or because there is too much data.
These patterns and trends can be collected and defined as a data mining model. Mining models can be
applied to specific scenarios, such as:
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )

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Bt9001, data mining

  • 1. Dear students get fully solved assignments Send your semester & Specialization name to our mail id : “ help.mbaassignments@gmail.com ” or Call us at : 08263069601 (Prefer mailing. Call in emergency ) ASSIGNMENT PROGRAM BSc SEMESTER V SUBJECT CODE & NAME Data Mining CREDIT 4 BK ID BT9001 MAX.MARKS 60 Note: Answer all questions. Kindly note that answers for 10 marks questions should be approximately of 400 words. Each question is followed by evaluation scheme. Q.1 What is the relation between data mining and data warehousing? Explain. Answer: - In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a system used for reporting and data analysis. Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. Q.2 Define data mining. State the differences between data mining and DBMS. Answer:- Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD),an interdisciplinary subfield of computer science, is the computational process of discovering patterns in
  • 2. large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the Q. 3 Explain clustering and its applications. Answer: - Application clustering (sometimes called software clustering) is a method of turning multiple computer servers into a cluster (a group of servers that acts like a single system). Clustering software is installed in each of the servers in the group. Each of the servers maintains the same information and collectively they perform administrative tasks such as load balancing, determining node failures, and assigning failover duty. The other clustering method, hardware clustering, requires that specialized hardware be installed in a single server that controls the cluster. 5 How data mining is used in telecommunication field? Explain Answer: - The telecommunications industry was one of the first to adopt data mining technology. This is most likely because telecommunication companies routinely generate and store enormous amounts of high-quality data, have a very large customer base, and operate in a rapidly changing and highly competitive environment. Telecommunication companies utilize data mining to improve their marketing efforts, identify fraud, and better manage their telecommunication networks. However, these companies also face a number of data mining challenges Q.6 Draw and explain the architecture of data mining based IDS and explain. Answer: - Data mining is the process of discovering actionable information from large sets of data. Data mining uses mathematical analysis to derive patterns and trends that exist in data. Typically, these patterns cannot be discovered by traditional data exploration because the relationships are too complex or because there is too much data. These patterns and trends can be collected and defined as a data mining model. Mining models can be applied to specific scenarios, such as: Dear students get fully solved assignments Send your semester & Specialization name to our mail id : “ help.mbaassignments@gmail.com ” or
  • 3. Call us at : 08263069601 (Prefer mailing. Call in emergency )