Meghana Deodhar

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Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microarray data analysis only a subset of the genes show cohesive expressions within a subset of the conditions/features. The existence of a large number of non-informative data points and features makes it challenging to hunt for coherent and(More)
For difficult classification or regression problems, practitioners often segment the data into relatively homogenous groups and then build a model for each group. This two-step procedure usually results in simpler, more interpretable and actionable models without any lossin accuracy. We consider problems such as predicting customer behavior across products,(More)
Most clustering algorithms are partitional in nature, assigning each data point to exactly one cluster. However, several real world datasets have inherently overlapping clusters in which a single data point can belong entirely to more than one cluster. This is often the case with gene microar-ray data since it is possible for a single gene to participate in(More)
For difficult classification or regression problems, practitioners often segment the data into relatively homogeneous groups and then build a predictive model for each group. This two-step procedure usually results in simpler, more interpretable and actionable models without any loss in accuracy. In this work, we consider problems such as predicting(More)
—Many data mining applications involve predictive modeling of very large, complex datasets. Such applications present a need for innovative algorithms and associated implementations that are not only effective in terms of prediction accuracy, but can also be efficiently run on distributed computational systems to yield results in reasonable time. This paper(More)
For difficult prediction problems, practitioners often segment the data into relatively homogenous groups and then build a model for each group. This two-step procedure usually results in simpler, more in-terpretable and actionable models without any loss in accuracy. We consider two important marketing problems, predicting customer-product preference and(More)
Several marketing problems involve prediction of customer purchase behavior and forecasting future preferences. We consider predictive modeling of large scale, bi-modal or multimodal temporal marketing data, for instance , datasets consisting of customer spending behavior over time. Such datasets are characterized by variability in purchase patterns across(More)
Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microar-ray data analysis only a subset of the genes show cohesive expressions within a subset of the conditions/features. The existence of a large number of non-informative data points and features makes it challenging to hunt for coherent and(More)