26 August 20151Data Mining 27/Sep/2008

Evolution of Database technology YEARPURPOSE 1960’sNetwork Model, Batch Reports 1970’sRelational data model, Executive information Systems 1980’sApplication specific DBMS(spatial data, scientific data, image data, …) 1990’sTerabyte Data warehouses, Object Oriented, middleware and web technology 2000’sBusiness Process 2010’sSensor DB systems, DBs on embedded systems, large scale pub/ sub systems 26 August 20152Data Mining

26 August 2015Data Mining3  Data explosion problem ◦ Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories  We are drowning in data, but starving for knowledge!  Solution: Data warehousing and data mining ◦ Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases Motivation : Necessity is the mother of invention

Why Data Mining? Data, Data, Data Every where …  I can’t find data I need – data is scattered over network  I can’t get the data I need  I can’t understand the data I need  I can’t use the data I found 26 August 20154Data Mining

 An abundance of data  Super Market Scanners, POS data  Credit cards transactions  Call Center records  ATM Machines  Demographic data  Sensor Networks  Cameras  Web server logs  Customer web site trails  Geographic Information System  National Medical Records  Weather Images  This data occupies  Terabytes- 10^12 bytes  Petabytes- 10^15 bytes  Exabytes- 10^18bytes  Zettabytes- 10^21bytes  Zottabytes-10^24bytes  Walmart- 24 Terabytes 26 August 20155Data Mining

 Process of sorting through large amounts of data and picking out relevant information  Process of analyzing data from different perspectives and summarizing it into useful information  Discovering hidden value in database  It is non-trivial process of identifying valid, novel, useful and understandable patterns in data  Extracting or mining knowledge from large amounts of data 26 August 20156Data Mining

26 August 2015Data Mining7 History Notes – Many Names of Data Mining YEARNamesUSES 1960 Data Fishing, Data Dredging Statisticians 1990 Data MiningDB Community, business 1989 Knowledge Discovery in databases AI, Machine Learning community Other Names Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction,

Data Warehousing provides the Enterprise with a memory Data Mining provides the Enterprise with intelligence 26 August 20158Data Mining

Why Data Mining?(Cont..) 26 August 20159Data Mining  Data Warehouse is single, complete and consistent store of data from variety of different sources available to end users  For example, AT and T handles billions of calls per day. Europe's Very Long Baseline Interferometer (VLBI) has 16 telescopes, each of which produces 1 Gigabit/second of astronomical data over a 25-day observation session  We need data mining for  Transforming data into useful information to users  Present data in useful format  Provide data access to business analyst, Information technology professionals

26 August 2015Data Mining10  Data Mining is the technique used to carry out KDD.  Data Mining turns data into information and then to knowledge Data Mining Process Information Data Knowledge

1.Data cleaning T o remove noise and inconsistent data 2. Data integration T o integrate (compile) multiple data sources 3. Data selection D ata relevant to analysis is selected 4. Data transformation S ummary normalization aggregation operations are performed (convert data into two dimension form) and consolidate the data Steps in Data Mining 26 August Data Mining

5. Data mining I ntelligent methods are applied to the data to discover knowledge or patterns 6. Pattern evaluation E valuation of the interesting patterns by thresholding 7. Knowledge Discovery V isualization and presentation methods are used to present the mined knowledge to the user. 26 August 2015Data Mining12 Steps in Data Mining(Cont..)

◦ Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation 26 August Data Mining

1. Classification Classification maps data into predefined groups or classes. It may be represented by methods such as decision trees, etc. Decision tree Flow chart like tree structure Each node denotes test of an attribute value Each branch represents outcome of test Leaves represent classes or class distribution. 26 August 2015Data Mining14 Data Mining Tasks

2. Regression Used to map a data item to a real valued prediction variable. Example. A manager wants to reach a certain level of savings before his retirement. Periodically he predicts his retirement savings by current value and several past values. He uses a simple linear regressive formula to predict the values of savings in future. 3. Prediction Many real world applications can be seen predicting future data states based on past and current data. Example - Predicting flooding is difficult problem 26 August 2015Data Mining15

4. Clustering Clustering is similar to classification except that the groups are not predefined. 5. Association Rule Association refers to uncovering relationship among data. Used in retail sales community to identify the items (products) that are frequently purchased together. 26 August 2015Data Mining Zzzz... Bread and Jam sell together!

6. Summarization Summarization of general characteristics or features of target class of data. Data characterization presented in various forms - pie charts, bar charts, curves. Data discrimination comparison of general features of target class of data objects with general features of objects from one or a set of contrasting classes. 7. Outlier Analysis Database may contain data objects that do not comply with general behavior model of data. These data objects are called as outliers. Data mining methods discard outliers as noise or exceptions. In applications such as fraud detection, rare events may be more interesting than regularly occurring events. 26 August 2015Data Mining17

 Relational data and transactional data  Text  Images, video  Mixtures of data Data Mining: Types of Data 26 August Data Mining

19  DataMind -- neurOagent  Information Discovery -- IDIS  SAS Institute -- SAS/Neuronets Data Mining Products 26 August 2015Data Mining

 RapidMiner and Weka – Defining data mining process  Top 8 data mining software in Angoss software 2.Infor CRM Epiphany 3.Portrait Software 4.SAS 5.SPSS 6.ThinkAnalytics 7.Unica 8.Viscovery 26 August Data Mining Data Mining Software

IndustryApplication FinanceCredit Card Analysis InsuranceFraud Analysis TelecommunicationCall record analysis Application Areas 26 August Data Mining

22  Financial Industry, Banks, Businesses, E-commerce ◦ Stock and investment analysis ◦ Identify loyal customers and risky customer ◦ Predict customer spending  Database analysis and decision support ◦ Market analysis and management  target marketing, customer relation management, market basket analysis. ◦ Risk analysis and management  Forecasting, quality control, competitive analysis ◦ Fraud detection and management Applications 26 August 2015

1.Intelligent Miner  It is IBM data mining product  Distinct feature is include scalability of its mining algorithm and tight integration with IBM DB2 related data base system. 2.DB Miner  Developed by DBMiner Technologies Inc.  Distinct features of DBMiner are Data cube based Online Analytical Mining Data Mining in Usage 26 August Data Mining

26 August 2015Data Mining24 India Product Sales Channel Regions RetailDirectSpecial Household Telecomm Video Audio Far East Europe The Telecomm Slice

26 August 2015Data Mining25  Data mining: discovering interesting patterns from large amounts of data  A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation  Mining can be performed in a variety of information repositories  Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier etc Conclusion

26 August 2015 Data Mining 26 Thank you !!!