By Thearling K.
This white paper presents an creation to the fundamental applied sciences of information mining. Examples of ecocnomic functions illustrate its relevance to state-of-the-art enterprise atmosphere in addition to a uncomplicated description of the way facts warehouse architectures can evolve to bring the worth of information mining to finish clients.
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Extra info for An Introduction to Data Mining
5 — Quinlan (1993) — Also used for rule induction 59 Age 100 Decision Tree Model no yes yes no 0 Dose 1000 60 30 One Benefit of Decision Trees: Understandability Age ? 35 Age < 35 Dose < 100 Y Dose ? 100 Dose < 160 N N Dose ? 160 Y 61 Supervised Algorithm Summary — kNN — Quick and easy — Models tend to be very large — Neural Networks — Difficult to interpret — Can require significant amounts of time to train — Rule Induction — Understandable — Need to limit calculations — Decision Trees — Understandable — Relatively fast — Easy to translate into SQL queries 62 31 Other Supervised Data Mining Techniques — Support vector machines — Bayesian networks — Naïve Bayes — Genetic algorithms — More of a search technique than a data mining algorithm — Many more...
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?
63 K-Means Clustering — User starts by specifying the number of clusters (K) — K datapoints are randomly selected — Repeat until no change: — Hyperplanes separating K points are generated Age 100 — K Centroids of each cluster are computed 0 Dose (cc’s) 1000 64 32 Self Organized Maps (SOM) O1 O2 ... 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 ...