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By: Pascal PERRY

Posted:

Text Clustering Algorithms Functions & Features

Feature and functions include Group documents based on their contents;Expectation-maximization clustering groups documents using spatial clustering techniques;Hierarchical clustering using Ward's agglomerative method to facilitate automatic grouping of documents into taxonomies;Groups documents into hierarchical clusters belong to one leaf cluster as well as its parent clusters;Cluster documents downstream in the process flow diagram using K-means or SOM/Kohonen clustering;Profiles clusters using additional structured data from original documents (age, purchase propensity, etc.);.. Text Clustering Algorithms Functions & Features are an important module of the comprehensive BI RFP Template.

The BI RFP template with Text Clustering Algorithms Functions & Features for BI is a Microsoft Excel spreadsheet (.xlsx) featuring a total of 1955 decision criteria. The BI RFP template allows you to quickly and easily prepare the business, functional, and technical requirements sections of the solicitation package you'll send to BI software vendors. It ensures for:

  • complete response from each BI software vendor
  • deep and accurate evaluation of submitted BI proposals
  • impartial side-by-side comparison of BI solutions evaluated
  • rational and documented selection of the best matching BI system

The BI software comparison report can save you up to 90% of the time you would have otherwise spent comparing all the BI solutions from scratch.

The Text Clustering Algorithms Functions & Features comprise the following elements:

  • Group documents based on their contents

    Group documents based on their contents, Text Clustering Algorithms Functions & Features
  • Expectation-maximization clustering groups documents using spatial clustering techniques

    Expectation-maximization clustering groups documents using spatial clustering techniques, Text Clustering Algorithms Functions & Features
  • Hierarchical clustering using Ward's agglomerative method to facilitate automatic grouping of documents into taxonomies

    Hierarchical clustering using Ward's agglomerative method to facilitate automatic grouping of documents into taxonomies, Text Clustering Algorithms Functions & Features
  • Groups documents into hierarchical clusters belong to one leaf cluster as well as its parent clusters

    Groups documents into hierarchical clusters belong to one leaf cluster as well as its parent clusters, Text Clustering Algorithms Functions & Features
  • Cluster documents downstream in the process flow diagram using K-means or SOM/Kohonen clustering

    Cluster documents downstream in the process flow diagram using K-means or SOM/Kohonen clustering, Text Clustering Algorithms Functions & Features
  • Profiles clusters using additional structured data from original documents (age, purchase propensity, etc.)

    Profiles clusters using additional structured data from original documents (age, purchase propensity, etc.), Text Clustering Algorithms Functions & Features

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Functions and Features