Analytic Solver Data Mining includes several different methods for data analysis such as
- Charting with 8 different types of available charts,
- Using Feature Selection for dimensionality reduction
- Apply Monte Carlo simulation methods, as well as charting like you find in @RISK or Analytic Solver Simulation, to analyze data
- Utilize transformation techniques which handle
- missing data,
- binning continuous data,
- creating dummy variables
- transforming categorical data
- Using Principal Components Analysis to reduce and eliminate superfluous or redundant variables
- Two different types of Clustering techniques, k-Means and Hierarchical.
- Perform Text Mining on a set of documents.
Exploring Your Data
Click the Explore icon to apply Monte Carlo simulation methods to your data, utilize Feature Selection to help decide which variables should be included in your classification or prediction models or use the Chart Wizard to create one or more charts of your data. The Feature Selection tool can help give insight into which variables are the most important or relevant for inclusion in your classification or prediction model using various types of statistics and data analysis measures. Analytic Solver Data Mining includes 8 different types of charts to choose from, including: bar charts, line charts, scatterplots, boxplots, histograms, parallel coordinates charts, scatterplot matrix charts or variable charts. This menu allows you to edit or view previously created charts as well.
Transforming Your Data
Click the Transformation icon when data manipulation is required. In most large databases or datasets, a portion of variables are bound to be missing some data. Analytic Solver Data Mining includes routines for dealing with these missing values by allowing a user to either delete the full record or apply a value of her/his choice. Analytic Solver Data Mining also includes a routine for binning continuous data for use with prediction and classification methods which do not support continuous data. Continuous variables can be binned using several different user specified options. Non-numeric data can be transformed using dummy variables with up to 30 distinct values. If more than 30 categories exist for a single variable, use the Reduce Categories routine to decrease the number of categories to 30. Finally, use Principal Components Analysis to remove highly correlated or superfluous variables from large databases.
Using Cluster Analysis
Click the Cluster icon to gain access to two different types of clustering techniques: k-Means clustering and hierarchical clustering. Both methods allow insight into a database or dataset by performing a cluster analysis. This type of analysis can be used to obtain the degree of similarity (or dissimilarity) between the individual objects being clustered.
Click the Text icon to use the Text Miner tool to analyze a collection of text documents for patterns and trends. (In the Cloud app, this tool is included in the Text section of the Ribbon.) These algorithms can categorize documents, provide links between documents that were not otherwise noted and create visual maps of the documents. Analytic Solver Data Mining takes an integrated approach to text mining by combining text processing and analysis in a single package. While Analytic Solver Data Mining is effective for mining “pure text” such as a set of documents, it is especially useful for “integrated text and data mining” applications such as maintenance reports, evaluation forms, or any situation where a combination of structured data and free-form text data is available.