ML and DM - Midterm Exam

One theoretical foundation of data mining is (Pattern discovery) reduce the data representation which trades accuracy for speed.

(Data compression) foundation of data mining is to compress the given data by encoding in terms of Bits, Clusters, etc.

Theoretical foundation of data mining that is to discover joint probability distributions of random variables is (inductive databases).

(Probability Theory) foundation of data mining is to discover patterns occurring in a database.

A data mining which uses statistics and converts data into numerical value is (Collaborative Filtering).

A statistical data mining which analyzes experimental data for two populations described by a numeric response variable is (Mixed-effect Models)

(Linear Model) is a method used to predict the value of the response variable from one or more predictor variable.

(Factor Analysis) statistica data mining is a method for analyzing time-series data.

Visual Data mining which data in database can be viewed in visual forms like curves, link graphs, 3-D cubes, etc. is (Visual Data Mining)

(Visual) Data mining makes use of audio signals to indicate the patterns of data.

(Statistical data mining) is used for recommending specific data based on the opinions of others gathered in data mining.

(Result) Visualization is a visual data mining that presents the several processes of data mining.

Data mining cannot be classified based on the Databases Mined. This statement is (False).

We can classify a data mining system according to the applications adapted. This statement is not (False).

(Loose Coupling) is a data mining integration scheme which the data mining system does not utilize any of the database or data warehouse functions.