Fuzzy C-mean Clustering using Data Mining

CHAPTER I - INTRODUCTION

• Background Study
• Data, Information and Knowledge

1.2.1 Data

1.2.2 Information

1.2.3 Knowledge

1.2.4  Data warehouses

• Data Mining

           1.3.1 Trends in Data Mining

• Process of Data Mining

          1.4.1 Classes

          1.4.2 Cluster

          1.4.3 Association

          1.4.4 Sequential Patterns

          1.4.5 Data Mining Consists Of Five Major Elements

• Clustering

          1.5.1 General Types of Clusters

• Cluster Analysis

     1.7 Classification of Clustering

     1.8 Fuzzy C- Means Clustering

     1.9 Chapter Layout

CHAPTER II - LITERATURE SURVEY

     2.1 Applications of Fuzzy C-Means Algorithm

     2.2 Summary

CHAPTER III - METHODOLOGY

     3.1 Existing System

           3.1.1 Disadvantages

     3.2 Problem Description

          3.2.1 Results

          3.2.2 Drawbacks

     3.3 Proposed System

          3.3.1  Fuzzy Sets and Membership Functions

          3.3.2  Fuzziness and Probability

          3.3.3  Clustering

          3.3.4  Difficulties with Fuzzy Clustering

          3.3.5   Objectives and Challenges

3.4 Proposed Performance Measures

         3.4.1   FCM Clustering with Varying Density

CHAPTER IV- IMPLEMENTATION

     4.1 Simulation Environment

     4.2 Experimental Results

CHAPTER V- CONCLUSION AND FUTURE ENHANCEMENT    

     5.1 Conclusion

     5.2 Future Enhancement

REFERENCES