2. 1 Online data mining course Chapter 1: Introduction László Pitlik University Gödöllő, Institute of Computer Sciences Gödöllő, H-2100 Páter K. u. 1. 2008.XII.04. Hello! My Name is Rob-But Ler, the Robot-Expert of My-X! My Boss is here as far as possible... Till then, you can replay his message:

3. 2 Greetings and introductions Welcome by the My-X project Apropos: „dress” rehearsal in frame of the best English courses of the world! Briefly about our symbols: and our keywords:sustainability, balance, equilibrium, consistency and finally about myself… Online data mining course – Chapter 1: Introduction

4. 3 Outline of the presentation Aims of this course A test-question (in advance and after that too) Further questions, to initialize the common thinking Theoretical background or near to the heresy?! Didactical background (how to learn?) List of the course units (what to learn?) Summary and conclusions One solution of the test-question Online data mining course – Chapter 1: Introduction

5. 4 Aims of the course On the basis of previous projects you can follow step by step, how to prepare (e.g. by pivot tables), and how to manage (s. OLAP) the necessary project databases, how to define similarity problems including their controlling aspects and how to make online and offline analyses and how to interpret and to describe the calculated results (as preferred) in an online expert system. By the end of the course you will know about each step for the successful managing of planning, decision making and forecasting. Online data mining course – Chapter 1: Introduction

6. 5 Test-Question (in advance) Please, „match” the following words, fragments and letters (one letter can be used not only once), and write a short story or (it is more comfortable for you, than) some equations based on the explored antagonisms: Sciencesyn, sin, sis Fusioncon, the C E I H T Y Online data mining course – Chapter 1: Introduction

7. 6 Initializing the common thinking Do you know, whether a prediction should be in general better for the shorter term or for the longer term? If possible: Vote ratio by the audience Do you know whether an analysis based on more data records should be more correct? If possible: Vote ratio by the audience Do you know whether an analysis testing through large amount of cases should be more fit than some other one without testing? If possible: Vote ratio by the audience Online data mining course – Chapter 1: Introduction

8. 7 Theoretical backgrounds OR near to the heresy? A phenomenon can only be labeled SCIENCE in case it can be transformed into program-codes (e.g. chess-robot). Each other phenomenon belongs to artistic performance (e.g. studies, lectures and always this presentation). The human intuition brings the good ideas. But not only human intuition seems to exist (cf. K. Lorenz, 1942). All living creatures on the earth have sensors to measure their (inside and outside) environment. The measured values are continuously interpreted in order to find some connection between causes and reactions. “Heureka”! – was already cried directly at the beginning of life! Data mining has to deliver possible connections based on the measured records. Therefore we can press our instinctive capability into source codes. Online data mining course – Chapter 1: Introduction

9. 8 Didactical background (how to learn) Sustainable education: Nothing irrelevant to store Strategic planning: consistency-based Operative thinking: market-oriented Priorities or core knowledge elements: Efficiency through real time analyses Case-Based Reasoning (CBR) logic as core method Most universal (benchmarking, forecasting / offline, online) Most adaptable (free to set parameters, no programming) Competition of methods and searching strategies Decisions trees Artificial neural networks Monte-Carlo Methods (MCM) and genetic algorithms Online data mining course – Chapter 1: Introduction

10. Learning strategies and their maintenance (source: own calculations)

11. 10 List of the course units (what to learn) The world can be interpreted in form of Object-Attribute-Matrixes (OAM)! Anomalies of the data assets management (Why is the preparation of an OAM so slow? How to avoid the anomalies?) Preparing OLAP (online analytical processing) databases (do it yourself, if nobody wants to make it) Using OLAP-techniques for OAM (efficiency as the highest priority) How it is made: Expert system (rules as universal solution) CBR-pattern (OAM from time series, or in benchmarking, or for production functions) Solver (be free offline) COCO (component based object comparison // be free online) Interpretations of results (chess-robots for context free situations) Standard expectations of studies (What you may not do and what have to do for a good study?) Online data mining course – Chapter 1: Introduction

12. 11 Summary We have defined strategic and operative aims (deriving from real problems)… We have checked, whether we see the same world around us… We would like to teach and learn only the most necessary competencies… We have seen in brief, which competencies we should combine in order to approximate a real time speed in the analysis… Online data mining course – Chapter 1: Introduction

13. 12 Conclusions We have data, methods, computers, networks, problems and unfortunately illogical restrictions in our General Problem Solving (GPS) strategies We have an icon: namely the chess-robot… * * * therefore * * * We should ensure the free access to each datum! We should learn from own instincts! We have to transform the intuitions into source codes! We have to provide the new methods also online! We have to teach the people to think instead to serve! We can detect the lacks of equilibrium! We can correct always the wrong directions! LET US DO THEM!

14. 13 Thank you very much for your attention! Further details: pitlik@miau.gau.hu http://miau.gau.hu/myx-free

15. 14 Pun?! pros and cons Science= Con-science (=TQM or con-sis-TENCY in thinking) Syn-the-sis= Fusion (of each thesis) Confusion= Sin - the-sis Sin<> ETHIC Syn-the-TIC=> Artificial (Intelligence) => Robotics http://en.wiktionary.org/wiki/conscience (incl. Etymology aspects)