By Thearling K.
This white paper presents an creation to the fundamental applied sciences of information mining. Examples of ecocnomic purposes illustrate its relevance to cutting-edge enterprise atmosphere in addition to a uncomplicated description of the way facts warehouse architectures can evolve to carry the price of knowledge mining to finish clients.
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Extra info for An Introduction to Data Mining
Org) — XML based (DTD) — Java Data Mining API spec request (JSR-000073) — Oracle, Sun, IBM, … — Support for data mining APIs on J2EE platforms — Build, manage, and score models programmatically — OLE DB for Data Mining — Microsoft — Table based — Incorporates PMML — It takes more than an XML standard to get two applications to work together and make users more productive 73 Data Mining Moving into the Database — Oracle 9i — Darwin team works for the DB group, not applications — Microsoft SQL Server — IBM Intelligent Miner V7R1 — NCR Teraminer — Benefits: — Minimize data movement — One stop shopping — Negatives: — Limited to analytics provided by vendor — Other applications might not be able to access mining functionality — Data transformations still an issue > ETL a major part of data management 74 37 SAS Enterprise Miner — Market Leader for analytical software — Large market share (70% of statistical software market) > 30,000 customers > 25 years of experience — GUI support for the SEMMA process — Workflow management — Full suite of data mining techniques 75 Enterprise Miner Capabilities Regression Models K Nearest Neighbor Neural Networks Decision Trees Self Organized Maps Text Mining Sampling Outlier Filtering Assessment 76 38 Enterprise Miner User Interface 77 SPSS Clementine 78 39 Insightful Miner 79 Oracle Darwin 80 40 Angoss KnowledgeSTUDIO 81 Usability and Understandability — Results of the data mining process are often difficult to understand — Graphically interact with data and results — Let user ask questions (poke and prod) — Let user move through the data — Reveal the data at several levels of detail, from a broad overview to the fine structure — Build trust in the results 82 41 User Needs to Trust the Results — Many models – which one is best?
I1 ... , unusual words) — Domain expertise — Linguistic analysis — Example: Cymfony BrandManager — Identify documents ? extract theme ? 5B in 2005 — Depends on what you call “data mining” — Less of a focus towards applications as initially thought — Instead, tool vendors slowly expanding capabilities — Standardization — XML > CWM, PMML, GEML, Clinical Trial Data Model, … — Web services? — Integration — Between applications — Between database & application 70 35 What is Currently Happening in the Marketplace?
I1 ... In O3 Oj — Like a feed-forward neural network except that there is one output for every hidden layer node — Outputs are typically laid out as a two dimensional grid (initial applications were in computer vision) 65 Self Organized Maps (SOM) O1 O2 ... I1 ... , unusual words) — Domain expertise — Linguistic analysis — Example: Cymfony BrandManager — Identify documents ? extract theme ? 5B in 2005 — Depends on what you call “data mining” — Less of a focus towards applications as initially thought — Instead, tool vendors slowly expanding capabilities — Standardization — XML > CWM, PMML, GEML, Clinical Trial Data Model, … — Web services?
An Introduction to Data Mining by Thearling K.