1. Ch. Eick: Some Ideas for Task4 Project2 Ideas on Creating Summaries that Characterize Clustering Results Focus: Primary Focus Cluster Summarization (what kind of objects does each cluster contain; what is unique about each particular cluster), Secondary focus: Relationships between clusters. Remark: Include the 13 th attribute when creating summaries. 1. Using a method of your choice (e.g. box plots), compare the distribution in a particular cluster with the distribution in the dataset: –Create summaries of clusters based on properties of a particular cluster that significantly deviate from the properties of the whole dataset. –Create interestingness scores for clusters based on the degree of deviation –Create summaries about major differences between clusters (analyze how one cluster differs from the other clusters) 2. Assess other relationship between different clusters (e.g. their distances) October 9, 2014

2. Ch. Eick: Some Ideas for Task4 Project2 Ideas on Creating Summaries and Evaluations of Clusterings II More Post Analysis Ideas: 3. Learn a decision tree (some other model) that separates the instances of a particular cluster from the instances of the other 4 clusters  Use the accuracy of the decision tree as a measure for the quality of a cluster  Use a highly pruned version of the decision tree as a summary of the decision tree (or rules derived from a decision tree; e.g. report all paths that lead to choose the class of cluster as a set of rules)  … 4. Using a method of your choice (e.g. box plots), compare the distribution of pairs of clusters: – Analyze which clusters are similar to each other and which deviate from each other. – Summarize the patterns they have in common and the patterns in which they differ. 5. Using a method of your choice (e.g. box plots), analyze how each cluster differs from the other 4 clusters.

3. Ch. Eick: Some Ideas for Task4 Project2 Example1: Using Box Plot Cluster Summaries 1. Compute the interquartile range (IQR) for each attribute for the dataset and for each cluster. 2. Compute the overlap  of each cluster box plot with the dataset boxplot. Let (a,b) be the cluster IQR with a>b and (a’,b’) the dataset IQR with a’>b’ for attribute att; then:  att =max(0, min(a’,a)-max(b’,b)) / (max(a’,a)-min(b’,b))) 3. Discard cluster box plot for att if  att >th (e.g. th=0.7) 4. Use the surviving boxplots as cluster summary for the clusters also reporting  for all clusters (including the discarded ones) 5. Compute cluster interestingness as follows: Let O= {  1,…,  r } be the overlap of a cluster c for its r attributes; in in general, Interestingness(c)=f(O); e.g. f(O)=average(O.values) Let v1, v2, v3 the lowest, second lowest, and third lowest value in O: Interestingness(O)=1- ((v1*3+v2*2+v3*1)/6)