A short note on Data Mining in an Organisation

1. Data mining in an organisation
Ms. Jaya Shankar
ME Computer Science, MBA
Assistant Professor
SNIT Business School
Adoor

2. Data Mining
• Definitions:
– Says about collecting, processing, storing and
analysing data in order to discover and extract
new informations from it.
– The practice of examining large pre-existing
databases in order to generate new information.
• Data mining helps organizations get the necessary
information needed to handle different processes as
quickly as possible.

3. Data Mining in HR
• With the help of data mining, HR departments are
becoming more efficient at making good decisions
for better hires and other functions. To get matching
candidates to needed positions is now easier, where
traditional methods take longer time for this.
• Through data mining, HR managers have been able
to keep track of absenteeism.
• Data mining helps for comparison and evaluation
purposes.
• Data mining improves the quality of human
performance and for management decision making
process.

4. Data Mining Sources
• Website Data Mining
– the use of research tools to get relevant data from
websites, e-commerce stores, online journals etc.
• Social Media Data Mining
– Data mining companies use social media to get the
required information to drive their
market/organizational analysis.
• Business Data Mining
– get data from company records on their working
personnel
• Resume Information Mining
– way of getting more balanced information about a
particular subject.

5. Data Mining Techniques
• Association (Relation Technique)
– Is one of the best techniques used.
– In this a pattern is discovered based on a
relationship between items in the same
transaction.
– This is used in market basket analysis to
identify a set of products that customers
frequently purchase together.

6. • Classification
– Is used to classify each item in a set of data into
one of a predefined set of classes or groups.
– Uses mathematical techniques such as decision
trees, linear programming, neural networks and
statistics.
– Example: classification can be applied to all
records of the employees who left the company
and can predict who will leave the company in
future period. In this case the lists can be
classified as “Leave” and “Stay”.

7. • Clustering
– Makes a meaningful or useful cluster of objects
which have similar characteristics.
– Example: Arrangement of books in library with
similar characteristics.
• Decision Tree
– The root of the tree is a simple condition that has
multiple answers which leads to other set of
questions with which a proper decision can be
made.

8. Other uses of Data Mining
• Market Segmentation
• Customer Churn
• Fraud Detection
• Direct Marketing
• Market Basket Analysis
• Trend Analysis

9. Thank You…..