PPT-1 CS 490 Sample Project  Mining the Mushroom Data Set

Kirk Scott 2 Yellow Morels 3 Black Morels 4 This set of overheads begins with the contents of the project checkoff sheet After that an example project is given 5 CS 490 Data Mining Project CheckOff Sheet. Clustering for . Market Segmentation . Presentation by Mike Calder . Clustering. Used for market segmentation. Researchers want to find groups that can be targeted with the same marketing strategy. Given data of which users click on certain adds, derive discriminative clusters. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Data . & Basic Data Analysis. Big Data . EveryWhere. ! . Lots of data is being collected . and warehoused . Web data, e-commerce. purchases at department/. grocery stores. Bank/Credit Card . transactions. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Another Introduction to Data Mining. Course Information. 2. Knowledge Discovery in Data [and Data Mining] (KDD). Let us find something interesting!. Definition. := . “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” . (Part 1). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.com. Objectives. Show use of Microsoft SQL Server 2008 Analysis Services Data Mining. Tantalise you with the power of DM. Kumar . Saminathan. Frequent Word Combinations Mining . and Indexing on . HBase. Introduction. Many projects on . HBase. . create indexes on multiple data. We are able to find the frequency of a single word easily . Mushroom Market report provides the future growth trend of the market based on in-depth research by industry experts.The global and regional market share along with market drivers and restraints are covered in the report. View More @ https://www.valuemarketresearch.com/report/mushroom-market The ZERI aims at providing affordable options that are viable and suitable to generate income and reduce poverty to people in Namibia146s rural and peri-urban communities CONTACTUniversity of NamibiaZ (. Security and Privacy in Online Social Networks). Bettina . Berendt. Department of Computer Science, KU Leuven, . Belgium. www.berendt.de. , . www.spion.me. . Thanks to (in more or less chronological order). Another Introduction to Data Mining. Course Information. 2. Knowledge Discovery in Data [and Data Mining] (KDD). Let us find something interesting!. Definition. := . “KDD is the non-trivial process of identifying valid, novel, potentially... Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.