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DOCUMENT CLUSTERING USING HIERARCHICAL ALGORITHM

Submitted in partial fulfillment of requirement for the V Sem MCA Mini Project Under Visvesvaraya Technological University, Belgaum Karnataka By Chandrakanth Nayak N (1RV09MCA11) Trikarandas (1RV09MCA55) Under the guidance of B.H . Chandrashekar

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DOCUMENT CLUSTERING USING HIERARCHICAL ALGORITHM

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  1. Submitted in partial fulfillment of requirement for the V Sem MCA Mini Project Under Visvesvaraya Technological University, Belgaum Karnataka By ChandrakanthNayak N (1RV09MCA11) Trikarandas (1RV09MCA55) Under the guidance of B.H. Chandrashekar Asst. Professor, Department of MCA RVCE DOCUMENT CLUSTERING USING HIERARCHICAL ALGORITHM

  2. The aim of the project is to implement the Hierarchical algorithm on dataset for document clustering, clustering algorithms are very much helpful in retrieval of information, web search engines are mainly dependent on clusters created by these types of algorithms, which helps in faster retrieval of queried document. • Create • Insert • Cluster • Delete Introduction

  3. The basic idea behind the project is collecting the dataset from the user and input those datasets to the hierarchic algorithm and process it to produce the output • Step-1.Start by assigning each item to a cluster, so that if you have N items in the table, you now have N clusters, each containing just one item. Let the distances (similarities) between the clusters the same as the distances (similarities) between the items they contain. • Step-2.Find the closest (most similar) pair of clusters and merge them into a single cluster, so that now you have one cluster less. • Step-3.Compute distances (similarities) between the new cluster and each of the old clusters. • Repeat steps 2 and 3 until all items are clustered into a single cluster of size N. (*) • Step 3 can be done in different ways, • Single-linkage, Complete-linkage and average-linkage clustering. Methodology

  4. Methodology

  5. Snapshot 1: Home page

  6. Snapshot 2: DataSet Creation

  7. Snapshot 3: Dataset value insertion

  8. Snapshot 4: Clustering-1

  9. Snapshot 5: Clustering-2

  10. Snapshot 6: Clustering-3

  11. Snapshot 7: Clustering-5

  12. Snapshot 8: Dataset Deletion

  13. Conclusion • Document Clustering using Hierarchical Clustering gives the implementation of real time clustering technique, and the hierarchical algorithm is implemented in small scale for different datasets which are stored in the database tables.

  14. Future Enhancements • Much more user friendly interface can be developed • Implementing the technique on real time documents • Support for customization of table structures

  15. Thank You

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