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Ideas on Creating Summaries and Evaluations of Clusterings. Focus: Primary Focus Summarization ( what kind of objects does each cluster contain ?), Secondary focus: Evaluation Post Analysis Ideas (related to Task 4 Project2):
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Ideas on Creating Summaries and Evaluations of Clusterings Focus: Primary Focus Summarization (what kind of objects does each cluster contain?), Secondary focus: Evaluation Post Analysis Ideas (related to Task 4 Project2): • 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 • Use evaluation measures (e.g. compactness, separation, Silhouette, purity) to evaluate the obtained cluster; problem: there are few useful internal evaluation measures out there.
Ideas on Creating Summaries and Evaluations of Clusterings II More Post Analysis Ideas: • 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) • … • 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. • …
Example1: Using Box Plot Cluster Summaries • Compute the interquartile range (IQR) for each attribute for the dataset and for each cluster. • 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))) • Discard cluster box plot for att if att>th (e.g. th=0.7) • Use the surviving boxplots as cluster summary for the clusters also reporting for all clusters (including the discarded ones) • 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)
