The document discusses frameworks for data mining (DM) research and argues that DM research should expand its focus beyond technology-oriented topics to also consider human and organizational aspects. It notes that while DM systems have potential business value, most research focuses on improving algorithms rather than how DM systems are developed and used in practice. The document advocates adopting some perspectives from information systems research which considers both technical and non-technical factors.

1. Competitive advantage from Data Mining: some lessons learnt in the Information Systems field Mykola Pechenizkiy , Seppo Puuronen Department of Computer Science University of Jyväskylä Finland Alexey Tsymbal Department of Computer Science Trinity College Dublin Ireland PMKD’05 Copenhagen, Denmark August 22-26, 2005

2. Outline Introduction and What is our message? Part I: Existing frameworks for DM Theory-oriented: Databases; Statistics; Machine learning; etc Process-oriented: Fayyad’s, CRISP, Reinartz’s Part II: Where we are? – rigor vs. relevance in DM Part III: Towards the new framework for DM research DM System as adaptive Information System (IS) DM research as IS Development: DM system as artefact DM success model: success factors KM Challenges in KDD One possible reference for new DM research framework Further plans and Discussion

3. What is Data Mining Data mining or Knowledge discovery is the process of finding previously unknown and potentially interesting patterns and relations in large databases (Fayyad, KDD’96) Data mining is the emerging science and industry of applying modern statistical and computational technologies to the problem of finding useful patterns hidden within large databases (John 1997) Intersection of many fields : statistics, AI, machine learning, databases, neural networks, pattern recognition, econometrics, etc.

4. H. Information Systems H.0 GENERAL H.1 MODELS AND PRINCIPLES H.2 DATABASE MANAGEMENT H.2.0 General Security, integrity, and protection H.2.8 Database Applications Data mining Image databases Scientific databases Spatial databases and GIS Statistical databases H.2.m Miscellaneous http://www.acm.org/class/1998/ valid in 2003

5. I. Computing Methodologies I.5 PATTERN RECOGNITION I.5.0 General I.5.1 Models Deterministic Fuzzy set Geometric Neural nets Statistical Structural I.5.2 Design Methodology Classifier design & evaluation Feature evaluation & selection Pattern analysis I.5.3 Clustering Algorithms Similarity measures I.5.4 Applications Computer vision Signal processing Text processing Waveform analysis I.2 ARTIFICIAL INTELLIGENCE I.2.0 General Cognitive simulation Philosophical foundations I.2.1 Applications and Expert Systems I.2.2 Automatic Programming I.2.3 Deduction and Theorem Proving I.2.4 Knowledge Representation Formalisms and Methods I.2.5 Programming Languages and Software I.2.6 Learning Analogies Concept learning Connectionism and neural nets Induction Knowledge acquisition Language acquisition Parameter learning I.2.7 Natural Language Processing I.2.m Miscellaneous

6. G. Mathematics of Computing G.3 PROBABILITY AND STATISTICS Correlation and regression analysis Distribution functions Experimental design Markov processes Multivariate statistics Nonparametric statistics Probabilistic algorithms (including Monte Carlo) Statistical computing

7. Our Message DM is still a technology having great expectations to enable organizations to take more benefit of their huge databases. There exist some success stories where organizations have managed to have competitive advantage of DM. Still the strong focus of most DM-researchers in technology-oriented topics does not support expanding the scope in less rigorous but practically very relevant sub-areas. Research in the IS discipline has strong traditions to take into account human and organizational aspects of systems beside the technical ones.

8. Our Message Currently the maturation of DM-supporting processes which would take into account human and organizational aspects is still living its childhood. DM community might benefit, at least from the practical point of view, looking at some other older sub-areas of IT having traditions to consider solution-driven concepts with a focus also on human and organizational aspects . The DM community by becoming more amenable to research results of the IS community might be able to increase its collective understanding of how DM artifacts are developed – conceived, constructed, and implemented, how DM artifacts are used, supported and evolved, how DM artifacts impact and are impacted by the contexts in which they are embedded.

9. Part I Existing Frameworks for DM Theory-oriented Databases; Statistics; Machine learning; Data compression Process-oriented Fayyad’s CRISP-DM Reinartz’s

10. Theory-Oriented Frameworks

11. Database Perspective DM as application to DBs “ In the same way business applications are currently supported using SQL-based API, the KKD applications need to be provided with application development support.” query KDD objects, support for finding NN s, clustering, or discretization and aggregate operations. Inductive databases approach query concept should be applied also to data mining and knowledge discovery tasks “ there is no such thing as discovery, it is all in the power of the query language” contain not only the data but the theory of the data as well Imielinski, T., and Mannila, H. 1996, A database perspective on knowledge discovery. Communications of the ACM , 39 (11), 58-64. Boulicaut, J., Klemettinen, M., and Mannila, H. 1999, Modeling KDD processes within the inductive database framework. In Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery , Springer-Verlag, London, 293-302

12. Reductionism Approach Two basic Statistical Paradigms “ Statistical Experiment” Fisher’s version, inductive principle of maximum likelihood Neyman and Pearson-Wald’s version, inductive behaviour Bayesian version, maximum posterior probability “ Statistical learning from empirical process” “ Structural Data Analysis” SVD Data mining  statistics - the issue of computational feasibility has a much clearer role in data mining than in statistics data mining area approaches that emphasize on database integration, simplicity of use, and the understandability of results theoretical framework of statistics does not concern much about data analysis as a process that includes several steps

13. Machine Learning Approach “ let the data suggest a model ” can be seen as a practical alternative to the statistical paradigm “ fit a model to the data ” Constructive Induction – a learning process, two intertwined phases: construction of the “best” representation space and generating hypothesis in the found space ( Michalski & Wnek , 1993). Feature transformation (PCA, SVD, Random Projection) Feature selection LSI

14. Data Compression Approach Compress the data set by finding some structure or knowledg e for it, where knowledge is interpreted as a representation that allows coding the data by using fewer amount of bits. Theories should not be ad hoc that is they should not overfit the examples used to build it. Occam’s razor principle,14th century. "when you have two competing models which make exactly the same predictions, the one that is simpler is the better". Mehta, M., Rissanen, J., and Agrawal, R. 1995, MDL-based decision tree pruning. In U.M. Fayyad, R. Uthurusamy (Eds.) Proceedings of the KDD 1995 , AAAI Press, Montreal, Canada, 216-221.

15. Other Theoretical frameworks for DM Microeconomic view the key point is that data mining is about finding actionable patterns: the only interest is in patterns that can somehow be used to increase utility ; a decision theoretic formulation of this principle: the goal can be formulated in finding a decision x that tries to maximise utility function f(x) . Kleinberg, J., Papadimitriou, C., and Raghavan, P. 1998, A microeconomic view of data mining, Data Mining and Knowledge Discovery 2 (4), 311-324 Philosophy of Science logical empiricism, critical rationalism, systems theory formism, mechanism, contextualism dispersive vs. integrative, analytical vs. synthetic theories subjectivist vs. objectivist, nomothetic vs. ideographic, nominalism vs. realism, voluntarism vs. determinism, epistemological assumptions Explanation, prediction, understanding

16. Process-Oriented Frameworks

17. Knowledge discovery as a process Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R., Advances in Knowledge Discovery and Data Mining , AAAI/MIT Press, 1997. I

18. CRISP-DM http://www.crisp-dm.org/

19. KDD: “Vertical Solutions” Reinartz, T. 1999, Focusing Solutions for Data Mining . LNAI 1623, Berlin Heidelberg.

20. Conclusion on different frameworks Reductionist approach of viewing data mining as statistics has advantages of the strong background, and easy-formulated problems. The data mining tasks concerning processed like clusterisation, regression and classification fit easily into these approaches. More recent (process-oriented) frameworks address the issues related to a view of data mining as a process, and its iterative and interactive nature

21. Part II Where we are? Rigor and Relevance in DM Reseach

22. So, where are we? Lin in Wu et al. notices that a new successful industry (as DM) can follow consecutive phases: discovering a new idea, ensuring its applicability, producing small-scale systems to test the market, better understanding of new technology and producing a fully scaled system. At the present moment there are several dozens of DM systems, none of which can be compared to the scale of a DBMS system. This fact indicates that we are still in the 3rd phase in the DM area!

23. Rigor vs Relevance in DM Research

24. Where is the focus? Still! … speeding-up, scaling-up, and increasing the accuracies of DM techniques. Piatetsky-Shapiro : “we see many papers proposing incremental refinements in association rules algorithms, but very few papers describing how the discovered association rules are used” Lin claims that the R&D goals of DM are quite different: since research is knowledge-oriented while development is profit-oriented. Thus, DM research is concentrated on the development of new algorithms or their enhancements, but the DM developers in domain areas are aware of cost considerations: investment in research, product development, marketing, and product support. However, we believe that the study of the DM development and DM use processes is equally important as the technological aspects and therefore such research activities are likely to emerge within the DM field.

25. Part III Towards the new framework for DM research

26. DMS in the Kernel of an Organization DM is fundamentally application-oriented area motivated by business and scientific needs to make sense of mountains of data. A DMS is generally used to support or do some task(s) by human beings in an organizational environment both having their desires related to DMS. Further, the organization has its own environment that has its own interest related to DMS, e.g. that privacy of people is not violated. Environment DM Task(s) DMS (Artifact) Organization

27. The ISs-based paradigm for DM Ives B., Hamilton S., Davis G. (1980). “A Framework for Research in Computer-based MIS” Management Science , 26 (9), 910-934. “ Information systems are powerful instruments for organizational problem solving through formal information processing” Lyytinen, K., 1987, “Different perspectives on ISs: problems and solutions.” ACM Computing Surveys , 19 (1), 5-46.

28. DM Artifact Development Adapted from: Nunamaker, W., Chen, M., and Purdin, T. 1990-91, Systems development in information systems research, Journal of Management Information Systems , 7 (3), 89-106. A multimethodological approach to the construction of an artefact for DM DM Artifact Development Experimentation Theory Building Observation

29. Research methods in a paper on DM Theoretical approach: theory creating Hypothesis, new algorithm, etc. Constructive approach Prototype of a DM tool Theoretical approach: theory testing and evaluation Artificial, benchmark, real-world data Evaluation techniques Conclusion on theory

30. The Action Research and Design Science Approach to Artifact Creation Design Knowledge Awareness of business problem Action planning Action taking Conclusion Business Knowledge Artifact Development Artifact Evaluation Contextual Knowledge

31. DM Artifact Use: Success Model 1 of 3 Adapted from D&M IS Success Models System Quality Information Quality Use User Satisfaction Individual Impact Organizational Impact Service Quality

32. DM Artifact Use: Success Model 2 of 3 What are the key factors of success ful use and impact of DMS both at the individual and organizational levels. how the system is used, and also supported and evolved, and how the system impacts and is impacted by the contexts in which it is embedded. Coppock: the failure factors of DM-related projects. have nothing to do with the skill of the modeler or the quality of data. But those do include: persons in charge of the project did not formulate actionable insights , the sponsors of the work did not communicate the insights derived to key constituents, the results don't agree with institutional truths the leadership, communication skills and understanding of the culture of the organization are not less important than the traditionally emphasized technological job of turning data into insights

33. DM Artifact Use: Success Model 3 of 3 Hermiz communicated his beliefs that there are the four critical success factors for DM projects: (1) having a clearly articulated business problem that needs to be solved and for which DM is a proper tool; (2) insuring that the problem being pursued is supported by the right type of data of sufficient quality and in sufficient quantity for DM; (3) recognizing that DM is a process with many components and dependencies – the entire project cannot be "managed" in the traditional sense of the business word; (4) planning to learn from the DM process regardless of the outcome , and clearly understanding, that there is no guarantee that any given DM project will be successful.

34. KM Perspective A knowledge-driven approach to enhance the dynamic integration of DM strategies in knowledge discovery systems. Focus here is on knowledge management aimed to organise a systematic process of (meta-)knowledge capture and refinement over time . knowledge extracted from data the higher-level knowledge required for managing DM techniques’ selection, combination and application Basic knowledge management processes of knowledge creation and identification, representation, collection and organization, sharing, adaptation, and application DEXA’05: TAKMA WS paper&presentation are available

35. New Research Framework for DM Research

36. Further Work Definition of Relevanc e concept in DM research The revision of the book chapter Further work on the new framework for DM research Organization of Workshop or Special Track or Working conference on more social directions in DM research likely with one of the focuses on IS as a sister discipline . Few options: IRIS Scandinavian Conference on IS is one option Next PMKD Workshop in Jyväskylä

37. Thank You! Book chapter draft is available on request from Mykola Pechenizkiy Department of Computer Science and Information Systems, University of Jyväskylä, FINLAND E-mail: [email_address] Tel.: +358 14 2602472 Fax: +358 14 260 3011 http://www.cs.jyu.fi/~mpechen Feedback is very welcome: Questions Suggestions Collaboration