Dear Scikit-learners, This is my first post here and I hope you experts can help me a lot.
We are using the agglomerative clustering with ward's linkage and connectivity constraint. The data size is around 205,000 (each is a single scalar feature). The data set is dynamic (in time) and we need to apply clustering at different time thorough the process. Initially all data is 0 and they increase gradually. Alternatively, in the early stage the data is more homogeneous and the heterogeneity among the data increases gradually. If the clustering is applied at the final stage (most heterogeneous data, but off course having patterns/clusters) requesting 20 clusters it takes only 61s of CPU time. But, if clustering is run in an early stage (more homogeneous data but all are not 0 and off course there are patterns/clusters in the data) with the same settings the time rises up to 1h 5m. The CPU time is in-between of these two if the data come from an in-between time stamp. I also tried the the other linkage options too, but the situation does not improve. My understanding is that the homogeneity is playing the role. Have you experienced this too? What solution do you suggest? Thanks in advance for your attention and help. -- Best regards Md. Khairullah PhD Student, KU Leuven Numerical Analysis and Applied Mathematics Section Celestijnenlaan 200a - box 2402 3001 Leuven room: 03.18 tel. +32 16 37 39 66 fax +32 16 3 27996
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