Full Version: A comparative study of various clustering algorithms in data mining
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Topic- A comparative analysis of various clustering algorithms in data mining

University - RTMNU(rashtrasant tukadoji maharaj nagpur university)

Project guide lines
Form the 5 types clustering algorithm partitioning based, hierarchical based, density based , grid based, model based we have to take one method from each of the types,
For ex. 1. Partitioning based :- k-means
             2. Hierarchical based :- BIRCH
             3. Density based :- DBSCAN
             4. Grid based :- CLIQUE or STING or Wave cluster
             5. Model based :- EM or COMWEB or SLINK
  Then the chosen algorithms has to be ompared on the following parameters:
Time complexity 
Space complexity 
Size of clusters 
Number of clusters
Shape of clusters
Density threshold 
Which data set is suitable 
Non-convex clusters
The steps will be like after running the code a gui box will appear where there it will ask which algorithm/algorithms to compare the below that a checkbox to compare of what parameters will be asked then at last with figures and table and some details the comparison should be displayed.