How Data Mining Works.
Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends.
It is used in credit risk management, fraud detection, and spam filtering.
It also is a market research tool that helps reveal the sentiment or opinions of a given group of people..
It's an advanced data analysis technique, combining machine learning and AI to extract useful information, which helps businesses learn more about customers' needs, increase revenues, reduce costs, improve customer relationships, and more..
Understand the data sets and data preprocessing.
Demonstrate the working of algorithms for data mining tasks such association rule mining, classification, clustering and regression.
Exercise the data mining techniques with varied input values for different parameters.
To obtain Practical Experience Working with all real data sets.
Open the Weka GUI Chooser.
Select EXPLORER present in Applications.
Go to OPEN file and browse the file that is already stored in the system “bank.csv”.
Thus the listing applications for the data mining was studied.
Aim:
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.
Multimedia Data Mining is a subfield of data mining that deals with an extraction of implicit knowledge, multimedia data relationships, or other patterns not explicitly stored in multimedia databases. 5.
What is text mining.
Text mining is the procedure of synthesizing information, by analyzing relations, patterns, and rules among textual data.
Understand the data sets and data preprocessing
Demonstrate the working of algorithms for data mining tasks such association rule mining, classification, clustering and regression
Exercise the data mining techniques with varied input values for different parameters
To obtain Practical Experience Working with all real data sets
The main idea of the algorithm is to use a divide and conquerstrategy: Compress the database which provides the frequent sets; then divide this compressed database into a set of conditional databases, each associated with a frequent set and apply data mining on each database
Multimedia Data Mining is a subfield of data mining that deals with an extraction of implicit knowledge, multimedia data relationships, or other patterns not explicitly stored in multimedia databases
5What is text mining? Text mining is the procedure of synthesizing information, by analyzing relations, patterns, and rules among textual data
