Meta S. Brown

Data mining is the way that ordinary businesspeople use a range of data analysis techniques to uncover useful information from data and put that information into practical use. Data miners don’t fuss over theory and assumptions. They validate their discoveries by testing. And they understand that things change, so when the discovery that worked like a charm yesterday doesn’t hold up today, they adapt.

The Bioinformatics Laboratory of the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, develops Orange in cooperation with an open source community. To open the sample data in Orange, follow these steps:Start Orange Canvas.University of Ljubljana does not offer support agreements.

RapidMiner is a small software and services firm focused on data mining. It offers a data-mining product with a visual programming interface. To open the sample data in RapidMiner, follow these steps:Start RapidMiner Studio.RapidMiner is an offshoot of the YALE development project of the Dortmund University of Technology (Germany).

Your first hands-on step with data is getting it from wherever it is to the place where you need it to be. Text formats are common, and you’re likely to encounter them often. One of the most common is comma-separated value (.csv) text. KNIME.com AG is a small software and services firm focused on data mining. It offers a data-mining product with a visual programming interface.

Because data miners lean heavily on basic graphs, some data-mining applications offer little or nothing more. Others provide a wide range of graph options, from the common to the exotic. It’s not necessary to use all of these, but you may benefit by selecting and using a few that suit your own needs. Data miners often use these graphs:Boxplot (also called box and whiskers)Histograms describe distributions of continuous variables, but have limited value for showing details.

Data miners work fast. One way to improve your productivity is to take full advantage of tools that let you do several things at once. It’s time-consuming (and boring) to set up a number of graphs separately, one at a time. So use these alternatives whenever you can:Data summariesTools that let you quickly ask for summaries of many variables, and get the summaries all at once.

Using codes for data reduces data entry time, prevents errors, and reduces the memory requirements for storing the data. But the codes aren’t meaningful unless you have documentation, or labels, to explain their meaning. Some data formats enable you to enjoy the advantages of using codes while keeping the information about the meaning of the codes in the same file.

University of Waikato faculty members develop tools as part of their work toward advancement of the field of machine learning. These tools are used in teaching, by scientists, and in industry. Weka is its general-purpose data-mining tool that offers a visual programming interface and a wide range of analytics capabilities.

When your data is in more than one place, you need ways to put it all together. When you join two datasets with different variables, you’re merging data. Merging is a common operation. Merging is used frequently in data mining, combining linked data such as Customer records and marketing campaign data Before and after test results Internal and vendor data To merge datasets, you must have a variable that identifies cases for matching; this is called a key or identifier variable.