Data Mining Applications

1.What is Data Mining?

ANSWER
Data mining (the analysis step of the knowledge discovery in databases process), is the process of discovering new patterns from large data sets. The overall goal of the data mining process is to extract knowledge from a data set in a human-understandable structure

2.What is Association rule mining?

ANSWER
Association rule mining searches for interesting relationships among items in given data set.

3.Differentiate between Data Mining and Data warehousing.

ANSWER
Data warehousing is merely extracting data from different sources, cleaning the data and storing it in the warehouse. Where as data mining aims to examine or explore the data using queries. These queries can be fired on the data warehouse. Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc. E.g. a data warehouse of a company stores all the relevant information of projects and employees. Using Data mining, one can use this data to generate different reports like profits generated etc

4.What are the different problems that “Data mining” can solve?

ANSWER
Data mining helps analysts in making faster business decisions which increases revenue with lower costs. Data mining helps to understand, explore and identify patterns of data. Data mining automates process of finding predictive information in large databases. Helps to identify previously hidden patterns.

5.Explain clustering algorithm.

ANSWER
Clustering algorithm is used to group sets of data with similar characteristics also called as clusters. These clusters help in making faster decisions, and exploring data. The algorithm first identifies relationships in a dataset following which it generates a series of clusters based on the relationships. The process of creating clusters is iterative. The algorithm redefines the groupings to create clusters that better represent the data.

6.Explain Association algorithm in Data mining?

ANSWER
Association algorithm is used for recommendation engine that is based on a market based analysis. This engine suggests products to customers based on what they bought earlier. The model is built on a dataset containing identifiers. These identifiers are both for individual cases and for the items that cases contain. These groups of items in a data set are called as an item set. The algorithm traverses a data set to find items that appear in a case. MINIMUM_SUPPORT parameter is used any associated items that appear into an item set.

7.How Apriori Algorithm Works?

ANSWER
Apriori is designed to operate on databases containing transactions. As is common in association rule mining, given a set of item sets, the algorithm attempts to find subsets which are common to at least a minimum number C of the item sets.

8.What is Clustering?

ANSWER
Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Clustering is a main task of explorative data mining

9.Define K-Means Algorithm?

ANSWER
In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.

10.What is OLTP?

ANSWER
OLTP is abbreviation of On-Line Transaction Processing. This system is an application that modifies data the instance it receives and has a large number of concurrent users.

11.What is OLAP?

ANSWER
OLAP is abbreviation of Online Analytical Processing. This system is an application that collects, manages, processes and presents multidimensional data for analysis and management purposes.