NeuroXL Clusterizer

Real-world data mining experts who rely on Microsoft Office Excel for their workflow could require a tool that would enable them to perform cluster analysis on their data. NeuroXL Clusterizer is a useful add-in that was created in order to be used with Excel, for deploying a neural network pattern for analyzing clustering in spreadsheets data. It will provide users with the means to identify patterns and clusterize the data selectively.

The add-in features a straightforward deployment process that requires no configuration or special setup steps. Once installed, it will be available for use under the Add-ins tab of users’ installation of Microsoft Excel.

When opening its main menu, one will be greeted with an intuitive three-tab array that will allow people to perform the selection for the cell range where the clustering will be deployed. Users can also select the number of clusters and define neural network parameters.

Those who require an in-depth analysis of their spreadsheet records distribution could benefit from the features of this add-in. Having its clustering algorithm based loosely on the architecture of the neural networks present in human brains, it allows one to define certain parameters that will control the output clusters.

People can select an initial learning rate, radius, weight or even epochs, all of which can be combined with several of its predefined functions: threshold, hyperbolic tangent, zero-based log-sigmoid or log-sigmoid. Nevertheless, in spite of its easy handling, novice users are advised to refer to the documentation, in order to fully understand its features.

For those who require an efficient tool for performing pattern recognition tasks in their data, this Excel add-in could be a wise choice. It will allow them to select custom cell ranges, define specific neural network parameters to control the output clusters or save their settings for further use. However, this add-in will require advanced knowledge of clustering and data distribution and inexperienced users are advised to refer to the documentation.