Data mining involves extracting useful patterns from large amounts of data. It uses software tools to analyze raw data and discover hidden patterns that can help businesses gain insights into customer behavior and develop effective marketing strategies. The key processes involved in data mining are data cleaning, integration, transformation, mining, pattern evaluation, and presentation. Data mining is commonly used for applications like classification, association, prediction, clustering, and regression. It has proven useful in domains such as healthcare for improving treatment and education for enhancing learning outcomes. Popular tools for data mining include Oracle Data Mining, IBM SPSS Modeler, RapidMiner, Apache Mahout, Weka, and KNIME.

1. DATA MINING

2. HISTORY:

3. DATA MINING:
Turn raw data into useful information(readable format).
By using a software to look for patterns in large batches of
data.
Businesses can use that data and learn more about their
customers to develop more effective marketing strategies,
sales etc…

4. PROCESSES:
Data Cleaning
Data integration
Data Transformation
Data Mining
Pattern Evolution
Data Presentation

5. KDD:
Knowledge Discovery from Data.
Growth of data: Terabytes to Petabytes
We are downing in data but starving for knowledge!
Fraud detection and detection of unusual patterns.

6. KDD PROCESSES:
Data Cleaning:
Removing noise and inconsistent data.
Data Integration:
Multiple data sources may be combined.
Data Selection:
Related data

7. KDD PROCESSES:
Data transformation
Unified Format
Data Mining
Extract patterns
Pattern Evaluation
Identifying true patterns
Data Presentation
Presenting data in a readable format

8. FLOW CAHRT FOR KDD:

9. WHAT KIND OF DATA??
Data from Databases.
Data from different resources.
Ex: Servers, Firewalls, log collectors etc….

10. TECHNIQUES USED:

11. CLASSIFICATION:
This technique helps to classify data in different classes.
Data handling, Ex: Multimedia, Text-data, Time-series data….
Data model involved: Ex: Databases
Analysis of data: Neural networks, ML, Statistics etc….

12. ASSOCIATION:
It creates rules that tells how often events are occurred together.
Ex: When a customer buys computer/device, then 90% of time they
will buy software’s.

13. PREDICTION:
Combination of other data mining techniques such as trends,
clustering, classification, etc.
It analyses past events or instanced in the right sequence to predict
a future event.

14. CLUSTERING:
This techniques helps to identify similar kind of data.
Recognizes differences and similarities between data.
Similar to classification technique, but involves grouping chunks of
data together based on their similarities.
Similar data is grouped in the same cluster.
Dissimilar data is grouped in the same cluster.

15. REGRESSION:
This techniques deals with prediction of values or number.
Planning and modeling data.
Ex: Predicting children’s height based on their age, weight, height
and other factors.

16. SEQUENTIAL PATTERNS:
Data evaluation will be done to discover sequential patterns.
It comprises of finding interesting subsequence in a set of
sequences, where the stake of a sequence can be measured in terms
of different criteria like length, occurrence frequency, etc.

17. DECISION TREES:
Supervised learning technique.
It is a tree that helps is in decision-making purposes.
The decision tree creates classification or regression models as a
tree structure.
It separates a data set into smaller subsets, and at the same time,
the decision tree is steadily developed. The final tree is a tree with
decision nodes and leaf nodes.

18. APPLICATIONS OF DATA MINING:

19. DATA MINING IN HEALTH CARE:
It holds incredible potential for healthcare services due to the
exponential growth in the number of electrical health records.
Previously Doctors and physicians hold patient information in the paper
where the data was quite difficult to hold.
Digitalization and innovation of new techniques reduce human efforts
and make data easily assessable.
For example, the computer keeps a massive amount of patient data with
accuracy and it improves the quality of the whole data management
system.

20. EDUCATIONAL DATA MINING:
This technique uses multiple algorithms to improve educational
results and explain educational procedures for further decision
making.
Learning initially began in the classroom and was based on
behavioural, psychological, and constructive models.
Behavioural models depend on observable changes in the behavior
of the student to determine the learning results.

21. TOOLS:
Oracle Data Mining
IBM SPSS Modeler
Rapid Miner
Apache Mahout
Weka
KNIME
SSQT(SQL Server Data tools)

22.  Oracle Data Mining
Oracle Data Mining(ODM), a component of the oracle Database, provides
powerful data mining algorithms that enable data analysts to discover insights, make
predictions and leverage their oracle data and investment.
 IBM SPSS Modeler
IBM SPSS Modeler is a data mining and text analytics software applications.
It is used to built predictive models and conduct other analytic tasks. It has a
visual interface which allows users to leverage statistical and data mining algorithms without
programming.

23.  Rapid Miner
RapidMiner is a data science software platform that provides an integrated
environment for the data preparation, machine learning, deep learning, text mining, and
predictive analytics. RapidMiner is developed on an open core model.
 Apache Mahout
Apache Mahout is a project of the Apache Software Foundation to produce free
implementations of distributed or otherwise scalable machine learning algorithms focused
primarily on linear algebra.

24.  WEKA
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms
can either be applied directly to a dataset or called from your own java code. Weka contains tools
for data pre-processing.
 KNIME
Stand for Konstanz information Miner, is a free and open-source data analytics, reporting
and integration platform. KNIME integrates various components for machine learning and data
mining through its modular data pipelining concept.

25. CONCLUSION:
The ultimate goal of data mining is the prediction of human
behaviour, and is by far the most common business application.
However this can easily be modelled to meet the objective of
detection and deterrence of criminals.
Data Mining tools increase not only the speed of analysis, but the
depth of its approach.

26. THANK YOU