【正文】 s (IDTs, Regression models) Interpretation and evaluation Konstanz, 49 EDBT2023 tutorial Intro ?Visualization tools can be very helpful ?sensitivity analysis (I/O relationship) ?histograms of value distribution ?timeseries plots and animation ?requires training and practice Response Velocity Temp Interpretation and evaluation Konstanz, 50 EDBT2023 tutorial Intro ?1989 IJCAI Workshop on KDD ?Knowledge Discovery in Databases (G. PiatetskyShapiro and W. Frawley, eds., 1991) ?19911994 Workshops on KDD ?Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, eds., 1996) ?19951998 AAAI Int. Conf. on KDD and DM (KDD’9598) ?Journal of Data Mining and Knowledge Discovery (1997) ?1998 ACM SIGKDD ?1999 SIGKDD’99 Conf. Important dates of data mining References general ? P. Adriaans and D. Zantinge. Data Mining. AddisonWesley: Harlow, England, 1996. ? M. S. Chen, J. Han, and P. S. Yu. Data mining: An overview from a database perspective. IEEE Trans. Knowledge and Data Engineering, 8:866883, 1996. ? U. M. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996. ? J. Han and M. Kamber. Data Mining: Concepts and Techniques. Man Kaufmann, 2023. To appear. ? T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of ACM, 39:5864, 1996. ? G. PiatetskyShapiro, U. Fayyad, and P. Smith. From data mining to knowledge discovery: An overview. In . Fayyad, et al. (eds.), Advances in Knowledge Discovery and Data Mining, 135. AAAI/MIT Press, 1996. ? G. PiatetskyShapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991. ? Michael Berry Gordon Linoff. Data Mining Techniques for Marketing, Sales and Customer Support. John Wiley Sons, 1997. ? Sholom M. Weiss and Nitin Indurkhya. Predictive Data Mining: A Practical Guide. Man Kaufmann, 1997. ? . Inmon, . Welch, Katherine L. Glassey. Managing the data warehouse. Wiley, 1997. ? T. Mitchell. Machine Learning. McGrawHill, 1997. Konstanz, 52 EDBT2023 tutorial Intro Main Web resources Konstanz, 53 EDBT2023 tutorial Intro Tutorial Outline ? Introduction and basic concepts ? Motivations, applications, the KDD process, the techniques ? Deeper into DM technology ? Decision Trees and Fraud Detection ? Association Rules and Market Basket Analysis ? Clustering and Customer Segmentation ? Trends in technology ? Knowledge Discovery Support Environment ? Tools, Languages and Systems ? Research challenges Konstanz, 54 EDBT2023 tutorial Intro ? 靜夜四無(wú)鄰，荒居舊業(yè)貧。 , February 28, 2023 ? 雨中黃葉樹，燈下白頭人。 2023年 2月 28日星期二 12時(shí) 25分 37秒 00:25:3728 February 2023 ? 1做前，能夠環(huán)視四周；做時(shí)，你只能或者最好沿著以腳為起點(diǎn)的射線向前。 。勝人者有力，自勝者強(qiáng)。 2023年 2月 28日星期二 上午 12時(shí) 25分 37秒 00:25: ? 1最具挑戰(zhàn)性的挑戰(zhàn)莫過(guò)于提升自我。 2023年 2月 28日星期二 12時(shí) 25分 37秒 00:25:3728 February 2023 ? 1空山新雨后，天氣晚來(lái)秋。 , February 28, 2023 ? 很多事情努力了未必有結(jié)果，但是不努力卻什么改變也沒有。 :25:3700:25Feb2328Feb23 ? 1故人江海別，幾度隔山川。) ? In particular: ?Jiawei HAN, Simon Fraser University, whose forthing book Data mining: concepts and techniques has influenced the whole tutorial ?Rajeev RASTOGI and Kyuseok SHIM, Lucent Bell Labs ?Daniel A. KEIM, University of Halle ?Daniel Silver, CogNova Technologies ? The EDBT2023 board who accepted our tutorial proposal Konstanz, 2 EDBT2023 tutorial Intro Tutorial goals ?Introduce you to major aspects of the Knowledge Discovery Process, and theory and applications of Data Mining technology ?Provide a systematization to the many many concepts around this area, according the following lines ?the process ?the methods applied to paradigmatic cases ?the support environment ?the research challenges ?Important issues that will be not covered in this tutorial: ?methods: time series, exception detection, neural s ?systems: parallel implementations Konstanz, 3 EDBT2023 tutorial Intro Tutorial Outline 1. Introduction and basic concepts 1. Motivations, applications, the KDD process, the techniques 2. Deeper into DM technology 1. Decision Trees and Fraud Detection 2. Association Rules and Market Basket Analysis 3. Clustering and Customer Segmentation 3. Trends in technology 1. Knowledge Discovery Support Environment 2. Tools, Languages and Systems 4. Research challenges Konstanz, 4 EDBT2023 tutorial Intro Introduction module outline ?Motivations ?Application Areas ?KDD Decisional Context ?KDD Process ?Architecture of a KDD system ?The KDD steps in short Konstanz, 5 EDBT2023 tutorial Intro Evolution of Database Technol