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Introduction and Review CS 636 – Adv. Data Mining CS 636 - Adv. Data Introduction and Review CS 636 – Adv. Data Mining CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS

Motivation: “Necessity is the Mother of Invention” n Data explosion problem n Automated data Motivation: “Necessity is the Mother of Invention” n Data explosion problem n Automated data generation and collection tools and mature database technology lead to tremendous amounts of data available and stored in information repositories n We are drowning in data, but starving for knowledge! n Solution: data mining and knowledge discovery n Extraction of interesting knowledge (rules, regularities, patterns, constraints) from large datasets CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 2

(R)evolution of Digital Hardware n Digitization of everything! n n Miniaturization of digital processors (R)evolution of Digital Hardware n Digitization of everything! n n Miniaturization of digital processors n n Images, videos, sound, measurements, etc Embedded chips are found everywhere creating, analyzing, and communicating digital information Digital storage technology n n Magnetic disks – exponential increase in size and decrease in cost (IBM’s breakthroughs in cramming more data unto a magnetic platter) Optical disks – from non-existence to ubiquity CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 3

What Is Data Mining? (1) n Data mining (knowledge discovery in datasets): n n What Is Data Mining? (1) n Data mining (knowledge discovery in datasets): n n Alternative names and their “inside stories”: n n n Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large datasets Data mining: a misnomer? Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. What is not data mining? n n (Deductive) query processing. Expert systems or small ML/statistical programs CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 4

What Is Data Mining? (2) n n The terms data mining and knowledge discovery What Is Data Mining? (2) n n The terms data mining and knowledge discovery are commonly used interchangeably, although KDD can be thought of as the process of knowledge discovery. KDD and data mining is a new, rapidly developing, multidisciplinary field n n n n AI Machine learning Statistics Database technology High-performance computing Visualization etc CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 5

Why Data Mining? — Potential Applications n Database analysis and decision support n Market Why Data Mining? — Potential Applications n Database analysis and decision support n Market analysis and management n n Risk analysis and management n n n target marketing, customer relation management (CRM), market basket analysis, cross selling, market segmentation Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and management Other Applications n Text mining (news group, email, documents) and Web analysis. n Intelligent query answering CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 6

Fraud Detection and Management (1) n Applications n n Approach n n widely used Fraud Detection and Management (1) n Applications n n Approach n n widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. use historical data to build models of fraudulent behavior and use data mining to help identify similar instances Examples n n n auto insurance: detect a group of people who stage accidents to collect on insurance money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network) medical insurance: detect professional patients and ring of doctors and ring of references CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 7

Fraud Detection and Management (2) n Detecting inappropriate medical treatment n n Detecting telephone Fraud Detection and Management (2) n Detecting inappropriate medical treatment n n Detecting telephone fraud n n n Australian Health Insurance Commission identifies that in many cases blanket screening tests were requested (save Australian $1 m/yr). Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm. British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud. Retail n Analysts estimate that 38% of retail shrink is due to dishonest employees. CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 8

Data Mining: A KDD Process Pattern Evaluation n Data mining: the core of knowledge Data Mining: A KDD Process Pattern Evaluation n Data mining: the core of knowledge discovery process. Data Mining Task-relevant Data Warehouse Selection Data Cleaning Data Integration Databases CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 9

Steps of a KDD Process n Learning the application domain: n n Creating a Steps of a KDD Process n Learning the application domain: n n Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation: n n summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation n n Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining n n relevant prior knowledge and goals of application visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 10

Data Mining Functionalities (1) n Concept description: Characterization and discrimination n n Generalize, summarize, Data Mining Functionalities (1) n Concept description: Characterization and discrimination n n Generalize, summarize, and contrast data characteristics, e. g. , dry vs. wet regions Association (correlation and causality) n n n Multi-dimensional vs. single-dimensional association age(X, “ 20. . 29”) ^ income(X, “ 20. . 29 K”) àbuys(X, “PC”) [support = 2%, confidence = 60%] contains(T, “computer”) àcontains(x, “software”) [1%, 75%] CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 11

Data Mining Functionalities (2) n Classification and Prediction n n Finding models (functions) that Data Mining Functionalities (2) n Classification and Prediction n n Finding models (functions) that describe and distinguish classes or concepts for future prediction E. g. , classify countries based on climate, or classify cars based on gas mileage n n n Presentation: decision-tree, classification rule, neural network Prediction: Predict some unknown or missing numerical values Cluster analysis n n Class label is unknown: Group data to form new classes, e. g. , cluster houses to find distribution patterns Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 12

Data Mining Functionalities (3) n Outlier analysis n Outlier: a data object that does Data Mining Functionalities (3) n Outlier analysis n Outlier: a data object that does not comply with the general behavior of the data n It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis n Trend and evolution analysis n n Sequential pattern mining, periodicity analysis n n Trend and deviation: regression analysis Similarity-based analysis Other pattern-directed or statistical analyses CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 13

Are All the “Discovered” Patterns Interesting? n A data mining system/query may generate thousands Are All the “Discovered” Patterns Interesting? n A data mining system/query may generate thousands of patterns, not all of them are interesting. n n Suggested approach: Human-centered, query-based, focused mining Interestingness measures: A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm n Objective vs. subjective interestingness measures: n Objective: based on statistics and structures of patterns, e. g. , support, confidence, etc. n Subjective: based on user’s belief in the data, e. g. , unexpectedness, novelty, actionability, etc. CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 14

Can We Find All and Only Interesting Patterns? n Find all the interesting patterns: Can We Find All and Only Interesting Patterns? n Find all the interesting patterns: Completeness n n n Can a data mining system find all the interesting patterns? Association vs. classification vs. clustering Search for only interesting patterns: Optimization n Can a data mining system find only the interesting patterns? n Approaches n n First general all the patterns and then filter out the uninteresting ones. Generate only the interesting patterns—mining query optimization CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 15

Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Statistics Data Mining Information Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Statistics Data Mining Information Science CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS Visualization Other Disciplines 16

Major Issues in Data Mining (1) n Mining methodology and user interaction n n Major Issues in Data Mining (1) n Mining methodology and user interaction n n Interactive mining of knowledge at multiple levels of abstraction n Incorporation of background knowledge n Data mining query languages and ad-hoc data mining n Expression and visualization of data mining results n Handling noise and incomplete data n n Mining different kinds of knowledge in databases Pattern evaluation: the interestingness problem Performance and scalability n Efficiency and scalability of data mining algorithms n Parallel, distributed and incremental mining methods CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 17

Major Issues in Data Mining (2) n Issues relating to the diversity of data Major Issues in Data Mining (2) n Issues relating to the diversity of data types n n n Handling relational and complex types of data Mining information from heterogeneous databases and global information systems (WWW) Issues related to applications and social impacts n n n Application of discovered knowledge n Domain-specific data mining tools n Intelligent query answering n Process control and decision making Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem Protection of data security, integrity, and privacy CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 18

Summary n n n Data mining: discovering interesting patterns from large amounts of data Summary n n n Data mining: discovering interesting patterns from large amounts of data A natural evolution of database technology, in great demand, with wide applications 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 and trend analysis, etc. n Classification of data mining systems n Major issues in data mining CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 19

A Brief History of Data Mining Society n 1989 IJCAI Workshop on Knowledge Discovery A Brief History of Data Mining Society n 1989 IJCAI Workshop on Knowledge Discovery in Databases (Piatetsky -Shapiro) n n 1991 -1994 Workshops on Knowledge Discovery in Databases n n n Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky. Shapiro, P. Smyth, and R. Uthurusamy, 1996) 1995 -1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’ 95 -98) n n Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) Journal of Data Mining and Knowledge Discovery (1997) 1998 ACM SIGKDD, SIGKDD’ 1999 -2001 conferences, and SIGKDD Explorations More conferences on data mining n PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc. CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 20

Reading n n Data Streams: Algorithms and Applications, S. Muthukrishnan, file: Muthu-Survey. pdf On Reading n n Data Streams: Algorithms and Applications, S. Muthukrishnan, file: Muthu-Survey. pdf On Approximate Algorithms for Data Mining Applications, F. N. Afraiti, file: approx_algos. pdf CS 636 - Adv. Data Mining (Wi 2004/05) - Asim Karim @ LUMS 21