Lluis Belanche + Alfredo Vellido. Intelligent Data Analysis and Data Mining

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1 Lluis Belanche + Alfredo Vellido Intelligent Data Analysis and Data Mining a.k.a. Data Mining II

2 Office 319, Omega, BCN EET, office 107, TR 2, Terrassa skype, gtalk: avellido Tels.: , /~belanche/docencia/aiddm/aiddm.html

3 Contents of the course disclaimer:(but who knows) 1. Introduction to DM and its methodologies 2. Visual DM: Exploratory DM through visualization 3. Pattern recognition 1 4. Pattern recognition 2 5. Feature extraction 6. Feature selection 7. Error estimation 8. Linear classifiers, kernels and SVMs 9. Probability in Data Mining 10. Nonlinear Dimensionality Reduction (NLDR) 11. Applications of NLDR: biomed & beyond 12. DM Case studies

4 2012/2013. Alfredo Vellido An Introduction to Mining (1)

5 What is DATA MINING? (1) Data Mining is the process of discovering actionable and meaningful patterns, profiles, and trends by sifting through your data using pattern recognition technologies ( ) is a hot new technology about one of the oldest processes of human endeavour: pattern recognition ( ) It is an iterative process of extracting knowledge from business transactions ( ) DM is the automatic discovery of usable knowledge from your stored data. Jesús Mena: Data Mining your Website (Digital Press, 1999, books.google)

6 What is DATA MINING? (2) Data Mining, by its simplest definition, automates the detection of relevant patterns in a database ( ) For many years, statisticians have manually mined databases ( ) DM uses well established statistical and machine learning techniques to build models that predict customer behaviour. Today, technology automates the mining process, integrates it with commercial data warehouses, and presents it in a relevant way for business users ( ) the leading DM products address the broader business and technical issues, such as their integration into complex IT environments. Berson, Smith, & Thearling: Building Data Mining Applications for CRM (McGraw Hill, 2000)

7 What is DATA MINING? (3) WIKIPEDIA 2005 DIXIT: Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" (1) and "The science of extracting useful information from large data sets or databases" (2). Although it is usually used in relation to analysis of data, data mining, like artificial intelligence, is an umbrella term and is used with varied meaning in a wide range of contexts. (1) W. Frawley and G. Piatetsky Shapiro and C. Matheus, Knowledge Discovery in Databases: An Overview. AI Magazine, 1992, (2) D. Hand, H. Mannila, P. Smyth: Principles of Data Mining. MIT Press, en.wikipedia.org/wiki/data_mining

8 What is DATA MINING? (4) WIKIPEDIA 06 DIXIT: Data mining (DM), also called Knowledge Discovery in Databases (KDD) or Knowledge Discovery and Data Mining, is the process of automatically searching large volumes of data for patterns such as association rules. It is a fairly recent topic in computer science but applies many older computational techniques from statistics, information retrieval, machine learning and pattern recognition.

9 What is DATA MINING? (5) In 1996, in the proceedings of the 1st International Conference on KDD, Fayyad gave one of the best known definitions of Knowledge Discovery from Data: The non trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. KDD quickly gathered strength as an interdisciplinary research field where a combination of advanced techniques from Statistics, Artificial Intelligence, Information Systems, and Visualization are used to tackle knowledge acquisition from large data bases. The term Knowledge Discovery from Data appeared in 1989 referring to the: [...] overall process of finding and interpreting patterns from data, typically interactive and iterative, involving repeated application of specific data mining methods or algorithms and the interpretation of the patterns generated by these algorithms.

10 What is DATA MINING? (6) WIKIPEDIA 08 DIXIT: Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods. It has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases." Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making.

11 What is DATA MINING? (7) WIKIPEDIA 10 gave up: BOTTOM LINE: The concept of DM, even if somehow well established, is still quite fluid

12 What to expect from a DM conference (good and bad examples, starting with a rather bad one) September 04: Wessex Institute of Technology (W.I.T.), Málaga, Spain

13 Data Mining 2004: Main Topics Sessions 1 & 2: Text Mining Session 3: Web Mining Session 4: Clustering Techniques Session 5: Data Preparation Techniques Session 6 & 7: Applications in Business, Industry and Government Session 8: Customer Relationship Management (CRM) Session 9 & 10: Applications in Science and Engineering

14 Data Mining 2007: Main Topics Session 1: Categorisation Methods Session 2: Data Preparation Session3: Enterprise InformationSystems Session 4: Clustering Techniques Session 5: National Security Session 6: Data and Text Mining Session 7: Mining Environmental and Geospatial Data Session 8: Applications in Business, Industry and Government

15 IDADM Data Mining 2008: Late years

16 Data Mining 2009: Late years Investigative Data Mining For Security And Criminal Detection Jesús Mena Butterworth Heinemann 2003

17 A different (good) conference, a different take IEEE CIDM 2012, Brussels 2012 IEEE Symposium on Computational Intelligence and Data Mining Data mining foundations Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis) Algorithms for new, structured, data types, such as arising in chemistry, biology, environment, and other scientific domains Developing a unifying theory of data mining Mining sequences and sequential data Mining spatial and temporal datasets Mining textual and unstructured datasets High performance implementations of data mining algorithms

18 A different conference, a different take IEEE CIDM 2012, Brussels 2012 IEEE Symposium on Computational Intelligence and Data Mining Mining in targeted application contexts Mining high speed data streams Mining sensor data Distributed data mining and mining multi agent data Mining in networked settings: web, social and computer networks, and online communities Data mining in electronic commerce, such as recommendation, sponsored web search, advertising, and marketing tasks

19 A different conference, a different take IEEE CIDM 2012, Brussels 2012 IEEE Symposium on Computational Intelligence and Data Mining Methodological aspects and the KDD process Data pre processing, data reduction, feature selection, and feature transformation Quality assessment, interestingness analysis, and post processing Statistical foundations for robust and scalable data mining Handling imbalanced data Automating the mining process and other process related issues Dealing with cost sensitive data and loss models Human machine interaction and visual data mining Security, privacy, and data integrity

20 A different conference, a different take IEEE CIDM 2012, Brussels 2012 IEEE Symposium on Computational Intelligence and Data Mining Integrated KDD applications and systems Bioinformatics, computational chemistry, geoinformatics, and other science & engineering disciplines Computational finance, online trading, and analysis of markets Intrusion detection, fraud prevention, and surveillance Healthcare, epidemic modeling, and clinical research Customer relationship management Telecommunications, network and systems management

21 But let s talk money... Where is the money in DM?

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32 What s DATA MINING?: A procedural viewpoint

33 What s DATA MINING?: A historicist viewpoint STATISTICS ESTADÍSTICA DM PATT RECOG KDD ARTIFICIAL INTELLIGENCE EXPERT SYSTEMS MACHINE LEARNING DB MANAGEMENT

34 What s DATA MINING?: A historicist viewpoint STATISTICS ESTADÍSTICA KDD ADVANCED PROBABILISTIC MODELS Probabilistic Models ARTIFICIAL INTELLIGENCE MACHINE LEARNING OTHERS Algor. Devel. Bio-plausible Models

35 DATA MINING as a methodology

36 CRISP: a DM methodology CRoss Industry Standard Process for Data Mining: neutral methodology from the point of view of industry, tool and application (free &nonproprietary) Pete Chapman, Randy Kerber (NCR); Julian Clinton, Thomas Khabaza, Colin Shearer (SPSS), Thomas Reinartz, Rüdiger Wirth (DaimlerChrysler) CRISP DM was conceived in 1996 DaimlerChrysler: leaders in industrial application, SPSS: leaders in product development (Clementine, 1994), NCR: owners of large (huge!) databases (Teradata) Financed by the EU. Version 1.0 released officially in 1999

37 CRISP: Hierarchic structure of the methodology

38 CRISP: Description of phases Problem/Business understanding: study of targets and requirements form the business/problem viewpoint. Defining it as a DM problem. Data understanding: data recolection; getting to know the data, trying to detect both quality problems and interesting features. Data preparation: Preparing the data set to be modelled, starting from raw data. This is an iterative and exploratory process. Selection of files, tables, variables, record samples plus data cleaning. Modelling: Data analysis using modelling techniques of a sort that are suitable for the problem at hand. Includes fiddling with the models, tuning their parameters, etc. Evaluation: All previous steps must be evaluated as whole (as a unitary process), and we must decide whether deliverables so far meet the DM challenge. Implementation: All the knowledge aquired to this point must be organized and presented to the client in a usable form. We must define, together with this client, a protocol to reliably deploy the DM findings.

39 CRISP: The virtuous loop of methodology phases

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