BY SANDY. WHAT IS DATAMINING TYPES OF DATAMINING TOOLS OVERVIEW OF TIBCO TIBCO SPOTFIRE MINER DATA ANALYSIS EXPLORE DATA MANIPULATE DATA CHART VIEW.

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Slide 1BY SANDY Slide 2 WHAT IS DATAMINING TYPES OF DATAMINING TOOLS OVERVIEW OF TIBCO TIBCO SPOTFIRE MINER DATA ANALYSIS EXPLORE DATA MANIPULATE DATA CHART VIEW Slide 3…

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Slide 1BY SANDY Slide 2 WHAT IS DATAMINING TYPES OF DATAMINING TOOLS OVERVIEW OF TIBCO TIBCO SPOTFIRE MINER DATA ANALYSIS EXPLORE DATA MANIPULATE DATA CHART VIEW Slide 3 Data mining automates the detection of relevant patterns in a database, using defined approaches and algorithms to look into current and historical data that can then be analyzed to predict future trends. Because data mining tools predict future trends and behaviors by reading through databases for hidden patterns, they allow organizations to make proactive, knowledge- driven decisions and answer questions that were previously too time-consuming to resolve. Slide 4 Most data mining tools can be classified into one of three categories: Traditional data mining tools Dashboards data mining tools Text-mining tools. Slide 5 TIBCO Spotfire Miner™ is a tool for enterprise-wide data mining that is designed to work seamlessly with the software you already use. You can import data from and export data to many sources, including spreadsheets such as Excel and Lotus, databases such as DB2, Oracle, and Sybase, and analytical software such as SAS and SPSS. TIBCO Spotfire Miner is launched from TIBCO SOFTWARE. Slide 6 Explore your data via charts, tabular displays, and descriptive statistics. Use Spotfire Miner’s tools for data cleaning and data manipulation to prepare your data for analytic model. Fit a variety of statistical models, including linear and logistic regression, and classification trees. Evaluate the effectiveness of your models with standard tools, such as lift charts. Slide 7 Create a network by dragging and dropping components from the explorer pane on the left to a worksheet in the desktop pane on the right to create the nodes of the network Slide 8 The data set used in this example is from the Duke University Cardiovascular Disease Databank and consists of 3504 patients and 6 variables. The patients were referred to Duke University Medical Center for chest pain. The goal of this exercise is simple: The six variables used in this dataset are as follows: sex 0 = male, 1 = female age of the patient, in years cad.dur the duration of the coronary event, in months cholesterol the measurement of the patient’s cholesterol level sigdz the presence (or absence) of significant coronary disease tvdlm the presence (or absence) of severe coronary disease.This is also called “three vessel” or “left main” disease. Slide 9 1. Double-click the Read Excel File node to open its properties dialog 2. Click Browse to display the Open dialog. 3. Because you previously clicked the Examples folder icon (at the lower left of the dialog), the Open dialog should display the examples/dukestudy folder. Slide 10 4.In the dukestudy folder, select the data file acath.xls, and click Open. (If your options are set to hide file extensions, the file name is displayed as acath.)In the Preview group, click Update Preview to display the first ten rows of the data (the default). 5. Click the Modify Columns tab. 6. Scroll down until you find sigdz and select it. 7. In the Set Types group, click Categorical. 8. Click OK to close the dialog Slide 11 Slide 12 Slide 13 Open the viewer for the Read Excel File node and examine the data you imported. 1. Click the Read Excel File node to select it, and then click the Viewer button on the Spotfire Miner toolbar. Slide 14 Slide 15 Slide 16 YOU THANK