TEXT CLUSTERING USING BISECTING K-MEANS ALGORITHM

• Implement the Bisecting K-Means algorithm.
• Deal with text data (news records) in document-term sparse matrix format.
• Design a proximity function for text data.
• Think about the Curse of Dimensionality.
• Think about best metrics for evaluating clustering solutions.

Conclusion:

Text clustering was performed successfully implementing K-Means and Bisecting K-Means algorithm. Same was evaluated using Silhouette Metric. The data was dealt using document-term sparse matrix and curse of dimensionality was cured using singular value decomposition.