Augustine S. Nsang

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This paper discusses one method of clustering a high dimensional dataset using dimensionality reduction and context dependency measures (CDM). First, the dataset is partitioned into a predefined number of clusters using CDM. Then, context dependency measures are combined with several dimensionality reduction techniques and for each choice the data set is(More)
Dimensionality reduction is the mapping of data from a high dimensional space to a lower dimension space such that the result obtained by analyzing the reduced dataset is a good approximation to the result obtained by analyzing the original data set. There are several dimensionality reduction approaches which include Random Projections, Principal Component(More)
This paper considers an existing computational method for minimum energy multicast in ad hoc wireless networks and proposes an empirical model that is based on Data Envelopment Analysis (DEA) methodology. The new model using input orientation with constant returns to scale (CRS) assumption is derived based on Economics concept of production function. The(More)
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