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UNIT 6 CLUSTER ANALYSIS  BASIC CONCEPTS AND ALGORITHMS  What Is Cluster Analysis  Different Types of Clustering, Different...
UNIT 6 CLUSTER ANALYSIS  BASIC CONCEPTS AND ALGORITHMS  What Is Cluster Analysis  Different Types of Clustering, Different...
Fig  Different ways of clustering same set of points  Cluster analysis The process of grouping data basing on the informat...
Fig  Different ways of clustering same set of points  Cluster analysis The process of grouping data basing on the informat...
Que 2 what are different types of clustering techniques  An entire collection of clusters is commonly referred to as clust...
Que 2 what are different types of clustering techniques  An entire collection of clusters is commonly referred to as clust...
3. Complete versus partial clustering Complete clustering  In this type of clustering, every data object is assigned to a ...
3. Complete versus partial clustering Complete clustering  In this type of clustering, every data object is assigned to a ...
3. Graph based cluster If the data is represented as a graph, where the nodes are objects and the links represent connecti...
3. Graph based cluster If the data is represented as a graph, where the nodes are objects and the links represent connecti...
K-Means Que 4  Explain K-Means partitioning Technique with example.  Or  What is partitioning method  Describe any one par...
K-Means Que 4  Explain K-Means partitioning Technique with example.  Or  What is partitioning method  Describe any one par...
Example1  Consider five points  X1,X2,X3,X4,X5  with the following coordinates as a two dimensional sample for clustering ...
Example1  Consider five points  X1,X2,X3,X4,X5  with the following coordinates as a two dimensional sample for clustering ...
              Figure 2 Now    A1   ,   A2   ,   A3    belongs to one cluster and    A4    and    A5    belongs to another ...
              Figure 2 Now    A1   ,   A2   ,   A3    belongs to one cluster and    A4    and    A5    belongs to another ...
          P Q Cluster to map A1 1.349 5.09 P A2 1.33 5.70 P A3 0.85 4.031 P A4 4.34 0.707 Q A5 5.40 0.707 Q Since    A1   ...
          P Q Cluster to map A1 1.349 5.09 P A2 1.33 5.70 P A3 0.85 4.031 P A4 4.34 0.707 Q A5 5.40 0.707 Q Since    A1   ...
Example 2  I recommend students to write this example in exam only if you have very-very less time  Consider samples 11,8,...
Example 2  I recommend students to write this example in exam only if you have very-very less time  Consider samples 11,8,...
Again, Move the elements to new centroids  5, 4,2,2,2  11, 8, 13, 10, 13, 12, 6, 10,11,6,7,13  .2  .9  1, 1 .1  Mean  1  M...
Again, Move the elements to new centroids  5, 4,2,2,2  11, 8, 13, 10, 13, 12, 6, 10,11,6,7,13  .2  .9  1, 1 .1  Mean  1  M...
Again, Move the elements to nearest centroid  1, 1  8, 6, 6, 7, 5, 4, 2, 2, 2  .4.25  .1  11, 13, 10, 13, 12, 10, 11, 13  ...
Again, Move the elements to nearest centroid  1, 1  8, 6, 6, 7, 5, 4, 2, 2, 2  .4.25  .1  11, 13, 10, 13, 12, 10, 11, 13  ...
SSE stands for sum of squared error.  Low SSE of clusters means good clusters. There are some techniques to reduce SSE, th...
SSE stands for sum of squared error.  Low SSE of clusters means good clusters. There are some techniques to reduce SSE, th...
Que 6  What is bisecting K-Means  Explain with Algorithm. Please note  Here, there are two algorithms. 1st one is from tex...
Que 6  What is bisecting K-Means  Explain with Algorithm. Please note  Here, there are two algorithms. 1st one is from tex...
Que7   Write a brief note on K-means and Different Types of Clusters. k- Means has some difficulties when clustering have ...
Que7   Write a brief note on K-means and Different Types of Clusters. k- Means has some difficulties when clustering have ...
Fig  K- Means with non-globular clusters  Que 8  Write the Strengths, Weaknesses, Time and space complexity of K-Means  St...
Fig  K- Means with non-globular clusters  Que 8  Write the Strengths, Weaknesses, Time and space complexity of K-Means  St...
Que 9   Write the procedure to handle document data for clustering. K-Means is not restricted to points and numbers. K-Mea...
Que 9   Write the procedure to handle document data for clustering. K-Means is not restricted to points and numbers. K-Mea...
PART 2 Agglomerative Hierarchical Clustering Que 10  What do you mean by Hierarchical clustering  What are different types...
PART 2 Agglomerative Hierarchical Clustering Que 10  What do you mean by Hierarchical clustering  What are different types...
Divisive hierarchical clustering Start with one i.e.  group all data objects into a single cluster, at each step, split a ...
Divisive hierarchical clustering Start with one i.e.  group all data objects into a single cluster, at each step, split a ...
Que 11  What is meant by cluster proximity  Explain Cluster proximity is defined as similarity or dissimilarity between el...
Que 11  What is meant by cluster proximity  Explain Cluster proximity is defined as similarity or dissimilarity between el...
Que 13  How to define proximity between two clusters using MIN technique   Or  Explain Singleexample   Link  hierarchical ...
Que 13  How to define proximity between two clusters using MIN technique   Or  Explain Singleexample   Link  hierarchical ...
In the above table, p3, p6 is having the lowest distance, so merge the data points into a single cluster.  P1 0 0.24 0.22 ...
In the above table, p3, p6 is having the lowest distance, so merge the data points into a single cluster.  P1 0 0.24 0.22 ...
The distance between  p3, p6  and  p2, p5  would be calculated as follows  dist   p3, p6 ,  p2, p5     MIN   dist p3, p2  ...
The distance between  p3, p6  and  p2, p5  would be calculated as follows  dist   p3, p6 ,  p2, p5     MIN   dist p3, p2  ...
The distance between  P2, P5, P3, P6  and P1 would be calculated as follows  dist   P2, P5, P3, P6 ,  p1      MIN   dist p...
The distance between  P2, P5, P3, P6  and P1 would be calculated as follows  dist   P2, P5, P3, P6 ,  p1      MIN   dist p...
Finally merge clusters  P2, P5, P3, P6, P4  and P1. The clusters and dendrogram are formed as follows   Fig  single link c...
Finally merge clusters  P2, P5, P3, P6, P4  and P1. The clusters and dendrogram are formed as follows   Fig  single link c...
After the clusters  P2, P5  and  P3, P6  are formed, the distances are calculated as follows  dist   p3, p6 ,  p2, p5     ...
After the clusters  P2, P5  and  P3, P6  are formed, the distances are calculated as follows  dist   p3, p6 ,  p2, p5     ...
dist   p3, p6, p4 ,  p1     MAX   dist p3, p1  , dist p6, p1 , dist p4, p1    MAX  0.22, 0.23, 0.37   0.37 dist   p2, p5 ,...
dist   p3, p6, p4 ,  p1     MAX   dist p3, p1  , dist p6, p1 , dist p4, p1    MAX  0.22, 0.23, 0.37   0.37 dist   p2, p5 ,...
proximity  ci, cj  of clusters ci,cj, which are of size mi and mj, respectively, is expressed by the following equation   ...
proximity  ci, cj  of clusters ci,cj, which are of size mi and mj, respectively, is expressed by the following equation   ...
Que 16  How to define proximity between two clusters using Group Average approach. The proximity between two clusters is d...
Que 16  How to define proximity between two clusters using Group Average approach. The proximity between two clusters is d...
3. Merging decisions are final In this type of clustering, once two clusters are nested, Reverting back is near impossible...
3. Merging decisions are final In this type of clustering, once two clusters are nested, Reverting back is near impossible...
DENSITY BASED CLUSTERING Que 19  Explain center based approach for density based clustering  In the center based approach,...
DENSITY BASED CLUSTERING Que 19  Explain center based approach for density based clustering  In the center based approach,...
Que 20   Explain DBSCAN algorithm in detail. DBSCAN  Density-Based Spatial Clustering of Applications with Noise  is a den...
Que 20   Explain DBSCAN algorithm in detail. DBSCAN  Density-Based Spatial Clustering of Applications with Noise  is a den...
 A point p is directly density-reachable from p2  p2 is directly densityreachable from p1  p1 is directly density-reachabl...
 A point p is directly density-reachable from p2  p2 is directly densityreachable from p1  p1 is directly density-reachabl...
Que 21   Write the strengths, weakness, time and space complexity of DBSCAN  Ans  Strengths  1  Resistant to noise 2  Can ...
Que 21   Write the strengths, weakness, time and space complexity of DBSCAN  Ans  Strengths  1  Resistant to noise 2  Can ...