Data Mining-B.C.A 17-20-First-Internal-Exam-FEB-2020

SAINTGITS COLLEGE OF APPLIED SCIENCES

          PATHAMUTTOM, KOTTAYAM

FIRST INTERNAL EXAMINATION, FEBRUARY 2020

Department of  V, Semester

DATA MINING

Total : 50 marks                                                         Time: 2 hours

Section A

Answer any 5 questions. Each question carries 2 marks.

1.    What is a data warehouse?

A data warehousing is defined as a technique for collecting and managing data from varied sources to provide meaningful business insights. ... It is electronic storage of a large amount of information by a business which is designed for query and analysis instead of transaction processing

2.What is a transactional database?

A transactional database consists of a file where each record represents a transaction.

2.    What is Prediction?

Prediction in data mining is to identify data points purely on the description of another related data value.

3.    What is outlier?

A value that "lies outside" (is much smaller or larger than) most of the other values in a set of data. For example in the scores 25,29,3,32,85,33,27,28 both 3 and 85 are "outliers".

5.What is classification?

Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks.

6. What is a data cube?

A data cube is generally used to easily interpret data. It is especially useful when representing data together with dimensions as certain measures of business requirements. A cube's every dimension represents certain characteristic of the database, for example, daily, monthly or yearly sales.

Section B

Answer any 5 questions. Each question carries 5 marks.

7.What are the features of a data warehouse?

The key characteristics of a data warehouse are as follows:

·         Some data is denormalized for simplification and to improve performance.

·         Large amounts of historical data are used.

·         Queries often retrieve large amounts of data.

·         Both planned and ad hoc queries are common.

·         The data load is controlled.

8. Explain data the architecture of a datamining system?

9.Explain different types of Normalization?

There are so many normalization techniques are there namely Min-Max normalization, Z-score normalization and Decimal scaling normalization. So by referring these normalization techniques we are going to propose one new normalization technique namely, Integer Scaling Normalization.

10.Explain world wide web mining?

Web mining can define as the method of utilizing data mining techniques and algorithms to extract useful information directly from the web, such as Web documents and services, hyperlinks, Web content, and server logs. The World Wide Web contains a large amount of data that provides a rich source to data mining.

11.Compare OLAP and OLTP?

OLTP is a transactional processing while OLAP is an analytical processing system. OLTP is a system that manages transaction-oriented applications on the internet for example, ATM. OLAP is an online system that reports to multidimensional analytical queries like financial reporting, forecasting, etc.

12.Explain data cleaning?

Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.

Section C

Answer any 1 questions. It carries 15 marks.

13. Explain data Pre processing Methods?

Data pre-processing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviours or trends, and is likely to contain many errors. Data pre-processing is a proven method of resolving such issues.

14. Explain various data mining functionalities?                     

Data Mining Functionalities. Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. Data mining tasks can be classified into two categories: descriptive and predictive. ... Predictive mining tasks perform inference on the current data in order to make predictions.

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