Hi all, Im doing Kmeans clustering in Mahout using Tanimoto distance measure
My input are feature vectors for which the indexes are the features and the value is 1 for features that exist in the sample, and 0 for non-existing features (it is actually clustering of users by documents they read, so for each user we have 1 in the documents that he read) So the input vectors are only 0 or 1 By the output clusters are double values - not only 0 and 1 and in the kmeans iterations I guess Kmeans move the cluster centers to various values for all features - not only 0 and 1 So will the Tanimoto distance measure work in this case? I think it only gives the Jaccard Index when the values are 0 and 1 (else it will not reflect the ratio between intersection and union of the features in the 2 points) If I add feature weights even more it will not be only 0 or 1 values given to the distance measure So will TanimotoDistanceMeasure really work in KMeans clustering in Hadoop? See this link for when Tanimoto is really a proper distance measure: http://en.wikipedia.org/wiki/Jaccard_index
