It contains 8th sem subject bigdata topic Data mining by author Anil maheshwari

1. Nandini Patil
Assistant Professor
Godutai Engg. College,kalaburagi
Data Mining

2. Introduction
 data mining is the art and science of discovering
the knowledge insights and patterns in the data.
 it is act of extracting useful pattern from an
organized Collection of data.
 patterns must be valid, novel, potentially useful
and understandable.
 data mining is a multidisciplinary field that
borrows techniques from a variety of field.
 it utilizes the knowledge of the data quality & data
organizing from the databases area.

3. Cont…
 It draws-
 Analytical techniques from statistics and computer
science
 knowledge of decision making from field of business
management.
 Example: customer who buy cheese & milk also
buy bread

4. Gathering & selecting data
 Growth of data is coming with higher velocity,
volume & variety.
 to learn from Data quality data needs to be
effectively gathered in the and organised and
then sufficiently mined.
 gathering and curating data takes time & efforts
when data is unstructured or semistructured.
 knowledge of the business domain helps to select
the right streams of the data for pursuing the new
insights.

5. Data cleaning & preparation
 Duplicate data needs to be removed
 Missing values need to be filled in
 Data element should be comparable
 Continuous values may need to be binned
 Outlier data elements need to be removed
 Ensure that the data is representative of the
phenomena
 Data may need to be selected to increase information
density

6. outputs of data mining
 data mining output servers different types of the
objective
 data mining output are
 decision tree
 regression evaluation or mathematical functions
 Some business rules

7. Evaluating data mining Results
predictive accuracy = correct predictions /Total predictions
predictive accuracy =(TP+TN)/(TP+TN+FP+FN)

8. Data mining techniques

9. Data mining techniques
 Decision Tree
 Regression
 artificial neural networks
 cluster analysis
 Association rules

10. Tools & platforms for data mining
 simple or a sophisticated
 standalone or embedded
 open source or a commercial
 User interface
 Data formats

11. comparison of popular data mining
platform

12. Data mining best practices
 Business understanding
 data understanding
 data preparation
 modeling
 model evaluation
 discrimination and rollout

13. CRISP-DM data mining cycle

14. Myths about Data Mining
 Myth #1 Data mining is about algorithms
 Myth #2 Data mining is about predictive accuracy
 Myth #3 Data mining requires a data warehouse
 Myth #4 Data mining requires large quantities of
data
 Myth #5 Data mining technology expert

15. Data mining mistakes
 Mistake #1 Selecting the wrong problem for data
mining
 Mistake #2 Buried under mountains of data
without clear metadata
 Mistake #3 Disorganized data mining
 Mistake #4 Insufficient business knowledge
 Mistake #5 Incompatibility of data mining tools
and datasets
 Mistake #6 Looking only at aggregated results
and not at individual records/predictions
 Mistake #7 Not measuring your results differently
from the way you are sponsor measures them