1. Introduction to Data Mining
Date: 26th February 2016
Special thanks: Han & Kamber

2. Syllabus Topics Introduction Classification of Data Mining System
Data Mining Architecture
Data Mining Functionalities
Major Issues in Data Mining
Importance of Data Mining
Application of Data Mining
Social Impacts of Data Mining

3. Introduction
Data???
Information???
Database???
DBMS???

4. Data Introduction Structured :DBMS Dhaval Gohel 40 50 60
Rishabh Chauhan 70 80
Mayur Padiya
Ankit Prajapati 30
Viral Prajapati 90
Semi –structured:XML
Unstructured:text
<Name>Dhaval Gohel</Name>
<CA>40</CA>
<IP>50</IP>
<CS>60</CS>
Dhaval Gohel,40,50,60
Rishabh Chauhan 60,70,80

5. Information Introduction Dhaval Gohel have 50% in current Sem.
Viral Prajapati have highest marks in Reaserch Skill.
Ankit Prajapti have lowest marks in CA.

6. Data base Introduction 120160107001 Dhaval Gohel 120160107002
Rishabh Chauhan
Mayur Padiya
Ankit Prajapati
Viral Prajapati
Dhaval Gohel
Dakor
Rishabh Chauhan
Modasa
Mayur Padiya
Nadiyad
Ankit Prajapati
Dehgam
Viral Prajapati
Naroda
Dhaval Gohel 40 50 60
Rishabh Chauhan 70 80
Mayur Padiya
Ankit Prajapati 30
Viral Prajapati 90

7. Introduction
DBMS

8. Introduction Data: row facts Information: processed data
Database: collection of organized related data
DBMS: set of software and tools used manipulate the database

9. What do you mean by Data Mining?
Data Mining: “ Data Mining is the process of discovering interesting knowledge from large amount of data stored in databases, data warehouses, or other information repositories. “
Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

10. Different Names of Data Mining
Knowledge discovery (mining) in databases (KDD)
knowledge extraction
data/pattern analysis
data archeology
data dredging
information harvesting
business intelligence, etc.

11. Database vs Data Mining
- Find all employee having salary >=50,000
- Find all the student who have attendance 0% last month
- Find all the Student who have Apple Laptop
Data Mining:
- Find all employee who is contractual (Classification)
- Find all the student who have attending lectures (Clustering)
- Find all the Student who have Apple Laptop and Apple Phone (Association Rule)

12. Classification of Data Mining System
Database technology
Information Science
Statistics
Machine Learning
Visualization
Other disciplines

13. Integration of Multiple Technolgy
Information
Science
Machine
Learning
Database
Technology
Statistics
Algorithms
Visualization
Data
Mining

14. Classification of Data Mining System
Classification is based on
Kind of database Mined:
Data model like relational, transactional, object- relational, or data warehouse.
Special types of data handled like spatial, time series, text, stream data, multimedia data mining system, or a World Wide Web mining system.

15. Classification of Data Mining System
Kind of knowledge Mined
Data Mining functionalities like Characterization and Discrimination, Mining Frequent Patterns, Classification and Prediction, Cluster Analysis, Outlier Analysis, Evolution Analysis
Data regularities vs data irregularities

16. Classification of Data Mining System
Kinds of techniques utilized
Degree of user iteration involved e.g., autonomous systems, interactive exploratory systems, query-driven system
Method of data analysis employed e.g., database-oriented or data warehouse oriented techniques, machine learning, statistics, visualization, pattern recongnization, neural networks, and so on.

17. Classification of Data Mining System
Application adapted
Finance, telecommunication, DNA, stock markets, and so on.

18. Knowledge Discovering form Data
Pattern Evaluation
Data Mining
Pattern
Task-relevant Data
Data transformations
Selection and Transformation
Preprocessed Data
Data Cleaning
Data Integration
Databases

19. KDD Process steps Cleaning: remove noise and inconsistent data
Integration: where multiple data sources may be combine
Selection: Data relevant to the analysis task are retrieved from the database
Transformation: Data are transformed into appropriate form for mining. Summary or aggregation operations
Data Mining: Various techniques like Association rule mining, Classification, Clustering are apply to Identify and count patterns
Pattern Evaluation: Identify truly interesting patterns representing knowledge base on some interestingness measure.
For example Support and Count for Association Rule Mining
Knowledge Presentation: Visualization and knowledge representation techniques are used to present the mined knowledge to the user

20. KDD Process on Web Log Data

22. KDD Process on Web Log Data
Cleaning: remove error logs
Integration: multiple logs may be combine
Selection: Data having valid Status and Media type is selected
Transformation: Transfer data to day wise, week wise
Data Mining: Identify Pattern and count frequent access
Pattern Evaluation: Display frequently access sequences
Knowledge Presentation: url page wise user count graph, IP address wise number of page visited count graph

23. Data Mining Architecture
Components
Databases, Data warehouse, World Wide Web or other Information repository
Database or Data warehouse server
Knowledge base
Data mining engine
Pattern Evaluation Module
User Interface

25. Data Mining Functionalities
Data Mining functionalities are used to specify the kind of patterns to be found in data mining tasks.
Task: Descriptive and Predictive
Descriptive: General Properties of data and database
Predictive: Perform inference (Conclusion) on the current data

26. Data Mining task

27. Data Mining Functionalities
Characterization and Discrimination
Mining Frequent Patterns
Classification and Prediction
Cluster Analysis
Outlier Analysis
Evolution Analysis

28. Characterization and Discrimination
Data Characterization is a summarization of the general characteristics or features of a target class of data.
For example: to analyze the improvements of the students who study in 2nd Semester ME in GECM and whose marks increased 5% in the current semester.
Display forms: pie charts, bar charts, multidimensional data cubes etc..

29. Characterization and Discrimination
Data Discrimination is a comparison of the general features of target class data objects with the general features of objects from one or a set of contrasting classes.
For example: faculties may like to compare the results of students who study in 2nd Semester ME in GECM and whose marks increased 5% and decreased 5% in the current semester .
Display forms: pie charts, bar charts, multidimensional data cubes etc..

30. Mining Frequent Patterns, Association Rule Mining
Frequent patterns are patterns that occur frequently in data set.
Forms: Frequent itemsets, subsequences, and substructures.
Frequent itemsets: ex. milk and bread.
Subsequence: ex. PC followed by Soft.
Substructure: sub graph, tress, or lattices

31. Mining Frequent Patterns, Association Rule Mining
Association Rule Mining is method use to find the interesting frequent pattern from large set of data items.
computer  antivirus [support=2%, Confidence=60%]
Support means that 2% of all the transactions in which computer and antivirus purchased together.
Confidence 60% means 60% of customers who purchased a computer also purchased antivirus together

32. Classification and Prediction
Classification is the process of finding a model (or function) that describes and distinguishes data classes or concepts. The model is derived based on the analysis of a set of training data and is used to predict the class label of objects for which the class label is unknown.
Classification is a two phase process
1) Lerning: Training data are analyzed by classification algorithm.
2) Classification: Classify data into the class lable.

33. Classification and Prediction
Prediction values continuous valued functions, i.e. it is used to predict missing or unavailable numeric data values rather than class labels. Regression analysis is a statistical method used numeric prediction.
Dhaval Gohel 40 50 60
Pass
Rishabh Chauhan 70 80
Mayur Padiya 30
Fail
Ankit Prajapati
Classification
Prediction

34. Classification and Prediction

35. Cluster Analysis
Clustering analyzes data objects without consulting class labels.
Clustering can be used to generate class labels for a group of data which did not exist at the beginning.
The objects are clustered or grouped based on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity.

36. Outlier Analysis
Outliers are data objects that do not comply with the general behavior or model of data. The analysis of outlier data is referred to as outlier mining.
Many data mining techniques discard outliers or exceptions as noise.
However, in some events these kind of events are more interesting. This analysis of outlier data is referred to as outlier analysis
ex: fraud detection.

37. Evolution Analysis
Data evolution analysis describes and models regularities or trends for objects whose behavior changes over time.
This may include characterization, discrimination, association and correlation analysis, classification, prediction or clustering of time related data.
Distinct features of such data include time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

38. Importance of Data Mining
Data collected in large data repositories become “data tombs”.
Data Mining tools perform data analysis and my uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific and medical research.
Data Mining tools turns data tombs into “Golden nuggets” of knowledge.

39. Application of Data Mining
Market analysis
Fraud detection
Customer retention
Production control
Science exploration

40. Major Issues in Data Mining
Mining different kinds of data
Handling multiple levels of abstraction
Incorporation of background knowledge
Visualization of mining results
Handling of incomplete or noisy data
Scalability of algorithms

41. Social Impacts of Data Mining
Privacy
Profiling
Unauthorized use