Advanced Data Mining: Introduction

Material Covered Chapter 1 from Ullman’s book. Many slides are from the “Data Mining: Concepts and Techniques” book. 2

3 Why Data Mining? The Explosive Growth of Data: from terabytes to petabytes –Data collection and data availability Automated data collection tools, database systems, Web, computerized society –Major sources of abundant data Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, YouTube We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets

from “Data Mining: Concepts and Techniques” 4 Evolution of Sciences Before 1600, empirical science s, theoretical science –Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. 1950s-1990s, computational science –Over the last 50 years, most disciplines have grown a third, computational branch (e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.) –Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models now, data science –The flood of data from new scientific instruments and simulations –The ability to economically store and manage petabytes of data online –The Internet and computing Grid that makes all these archives universally accessible –Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes. Data mining is a major new challenge! Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science, Comm. ACM, 45(11): 50-54, Nov. 2002

5 What Is Data Mining? Data mining (knowledge discovery from data) –Extraction of interesting ( non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data –Data mining: a misnomer? Alternative names –Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, business intelligence, etc. Watch out: Is everything “data mining”? Negative examples: –Simple search and query processing –(Deductive) expert systems from “Data Mining: Concepts and Techniques”

6 Knowledge Discovery (KDD) Process This is a view from typical database systems and data warehousing communities Data mining plays an essential role in the knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation from “Data Mining: Concepts and Techniques”

7 Data Mining in Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems from “Data Mining: Concepts and Techniques”

Directions in modeling Pattern extraction  Model Discovery Statistical modeling –E.g., decide that the data comes from a Gaussian distribution, estimate μ,σ parameters. Machine learning –Train an algorithm, then apply to new data. Results of Complex Queries (computational approaches) –E.g., summarization of the importance of a webpage in the form of a “pagerank” value. –E.g., prominent feature extraction, such as frequent itemsets and similar items. 8

9 Multi-Dimensional View of Data Mining Knowledge to be mined (or: Data mining functions) –Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. –Descriptive vs. predictive data mining –Multiple/integrated functions and mining at multiple levels Data to be mined –Database data (extended-relational, object-oriented, heterogeneous, legacy), data warehouse, transactional data, stream, spatiotemporal, time- series, sequence, text and web, multi-media, graphs & social and information networks Techniques utilized –Data-intensive, data warehouse (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance, etc. Applications adapted –Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. from “Data Mining: Concepts and Techniques”

Meaningfulness of patterns A big data-mining risk is that you will “discover” patterns that are meaningless. Bonferroni’s principle: (roughly) if you look in more places for interesting patterns than your amount of data will support, you are bound to find meaningless patterns 10

Rhine Paradox Joseph Rhine was a parapsychologist in the 1950’s who hypothesized that some people had Extra-Sensory Perception He devised an experiment where subjects were asked to guess 10 hidden cards – red or blue He discovered that almost 1 in 1000 had ESP – they were able to get all 10 right! He told these people they had ESP and called them in for another test of the same type Alas, he discovered that almost all of them had lost their ESP What did he conclude? He concluded that you shouldn’t tell people they have ESP; it causes them to lose it! 11

12 Major Challenges in Data Mining Efficiency and scalability of data mining algorithms Parallel, distributed, stream, and incremental mining methods Handling high-dimensionality Handling noise, uncertainty, and incompleteness of data Incorporation of constraints, expert knowledge, and background knowledge in data mining Pattern evaluation and knowledge integration Mining diverse and heterogeneous kinds of data: e.g., bioinformatics, Web, software/system engineering, information networks Application-oriented and domain-specific data mining Invisible data mining (embedded in other functional modules) Protection of security, integrity, and privacy in data mining from “Data Mining: Concepts and Techniques”

Kdnuggets polls

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Things Useful to Know Probability Linear Algebra basics Hash functions Indices Secondary storage Power laws 16

Big Data Sizes: Tiny  0s Small  1000s fitting in memory Medium  (may not fit in memory) Large  Huge  From Graefe’s “New algorithms for join and grouping operations”,