A comparative study of various clustering algorithms in data mining
10-23-2019, 07:43 PM,
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A comparative study of various clustering algorithms in data mining
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 Agglomerative Accuracy 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. |
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