Can I use entropy as a measure for determining significant variables in a cluster after clustering?Datamodel for cluster analysis terms & segmentationHow can i modify k-means so it can use for incremental text clustering?How can autoencoders be used for clustering?How to measure F1 score and NMI for clustering task?How can I plot data after cluster it into two cluster?Why use SOM for clustering?Get values from k-means cluster after clusteringCan grid-based clustering method be use for customer segmentation?Similarity measure before and after dimensionality reduction or clusteringClustering for variables with large amount of categories
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Can I use entropy as a measure for determining significant variables in a cluster after clustering?
Datamodel for cluster analysis terms & segmentationHow can i modify k-means so it can use for incremental text clustering?How can autoencoders be used for clustering?How to measure F1 score and NMI for clustering task?How can I plot data after cluster it into two cluster?Why use SOM for clustering?Get values from k-means cluster after clusteringCan grid-based clustering method be use for customer segmentation?Similarity measure before and after dimensionality reduction or clusteringClustering for variables with large amount of categories
$begingroup$
After clustering my data into k groups, I would like to determine for each of the clusters, which dimensions(variables) significantly describe that particular cluster. For example, lets say cluster A contains a majority of people from one age group purchasing a vehicle from particular segment. Cluster B contains people belonging to various age groups but majorly from one or two regions (out of 10-15 regions).
Hypothesis: Using entropy, determine for each cluster, which variables are homogeneous and use those variables to create a definition for that cluster by computing the mode (or some other descriptive statistic). For example,
Cluster A: Customers of 30-40yrs age group, purchasing vehicles from segment C1, etc
Cluster B: Customers from region R1, earning 30K-40K per annum, etc
Are there any pitfalls of using this technique?
clustering
asked 11 mins ago
Rohit GavvalRohit Gavval
708
$endgroup$
add a comment |
$begingroup$
After clustering my data into k groups, I would like to determine for each of the clusters, which dimensions(variables) significantly describe that particular cluster. For example, lets say cluster A contains a majority of people from one age group purchasing a vehicle from particular segment. Cluster B contains people belonging to various age groups but majorly from one or two regions (out of 10-15 regions).
Hypothesis: Using entropy, determine for each cluster, which variables are homogeneous and use those variables to create a definition for that cluster by computing the mode (or some other descriptive statistic). For example,
Cluster A: Customers of 30-40yrs age group, purchasing vehicles from segment C1, etc
Cluster B: Customers from region R1, earning 30K-40K per annum, etc
Are there any pitfalls of using this technique?
clustering
asked 11 mins ago
Rohit GavvalRohit Gavval
708
$endgroup$
add a comment |
$begingroup$
After clustering my data into k groups, I would like to determine for each of the clusters, which dimensions(variables) significantly describe that particular cluster. For example, lets say cluster A contains a majority of people from one age group purchasing a vehicle from particular segment. Cluster B contains people belonging to various age groups but majorly from one or two regions (out of 10-15 regions).
Hypothesis: Using entropy, determine for each cluster, which variables are homogeneous and use those variables to create a definition for that cluster by computing the mode (or some other descriptive statistic). For example,
Cluster A: Customers of 30-40yrs age group, purchasing vehicles from segment C1, etc
Cluster B: Customers from region R1, earning 30K-40K per annum, etc
Are there any pitfalls of using this technique?
clustering
asked 11 mins ago
Rohit GavvalRohit Gavval
708
$endgroup$
After clustering my data into k groups, I would like to determine for each of the clusters, which dimensions(variables) significantly describe that particular cluster. For example, lets say cluster A contains a majority of people from one age group purchasing a vehicle from particular segment. Cluster B contains people belonging to various age groups but majorly from one or two regions (out of 10-15 regions).
Hypothesis: Using entropy, determine for each cluster, which variables are homogeneous and use those variables to create a definition for that cluster by computing the mode (or some other descriptive statistic). For example,
Cluster A: Customers of 30-40yrs age group, purchasing vehicles from segment C1, etc
Cluster B: Customers from region R1, earning 30K-40K per annum, etc
Are there any pitfalls of using this technique?
clustering
clustering
asked 11 mins ago
Rohit GavvalRohit Gavval
708
asked 11 mins ago
Rohit GavvalRohit Gavval
708
asked 11 mins ago
Rohit GavvalRohit Gavval
708
asked 11 mins ago
Rohit GavvalRohit Gavval
708
asked 11 mins ago
Rohit GavvalRohit Gavval
708
708
add a comment |
add a comment |
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