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)
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Intensive Care Unit (ICU) monitors generate large volumes of high frequency data from numerous cardiac and respiratory sensors attached to a patient. This presents information overload to medical staff who need to interpret this data to evaluate the physiological status of the patient at any particular point in time. In this paper we present a machine… (More)
This paper considers two approaches to query-based dimensionality reduction. Given a data set, D, and a query, Q, the first approach performs a random projection on the dimensions of D that are not in Q to obtain the data set D R. A new data set (D RQ) is then formed comprising all the dimensions of D that are in the query Q together with the dimensions of… (More)