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Activity Number: 614
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #317623 View Presentation
Title: A Pseudo-Supervised Clustering Approach
Author(s): Xinying Mu* and Mark Kon
Companies: Boston University and Boston University
Keywords: pseudo-supervised clustering ; supervised classification ; confusion matrix ; graph partition
Abstract:

Standard clustering builds models based on distance connectivity. We investigate an alternative clustering method using a so-called pseudo-supervised approach. This optimizes over all possible cluster partition of the data by scoring a partitioning as the accuracy of a machine learning (ML) algorithm in separating the clusters (now viewed as fixed classes) based on ML training and testing. However, this involves a very computationally intensive optimization. We have an algorithm that hybridizes the pseudo-supervised approach with standard clustering, using a graph-based cluster model. We take a large data set and divide it into n small clusters by standard clustering, and then aggregate these n clusters into m (m < n) larger clusters. The aggregation is done using a variant of the above pseudo-supervised method, by identifying a confusion matrix (using machine classifiers such as SVM and random forest) among the n classes obtained in the first clustering step, and using this as a basis for graph clustering. We discuss this algorithm theoretically, and apply it to classifying cancer data sets based on gene expression and spectral bio-marker data.


Authors who are presenting talks have a * after their name.

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