ABSTRACT
In this project, we shall implement the hierarchical clustering algorithm and apply it to various data sets such as the weather data set, the student data set, and the patient data set. We shall then reduce these datasets using the following dimensionality reduction approaches: Random Projections (RP), Principal Component Analysis (PCA), Variance (Var), the New Random Approach (NRA), the Combined Approach (CA) and the Direct Approach (DA). The rand index and ARI will be implemented to measure the extent to which a given dimensionality reduction method preserves the hierarchical clustering of a data set. Finally, the six reduction methods will be compared by runtime, inter-point distance preservation, variance preservation and hierarchical clustering preservation of the original data set.
ABSTRACT
Therapeutic effects of the methanolic root extract and a combination of leaf and root extracts of Morinda lucida were evaluated...
Abstract: This research explores the role of competency frameworks in aligning vocational e...
ABSTRACT
The growth and spread of internet with an extraordinary pace over the last few decades has resulted in its increased use for mar...
ABSTRACT
Copyright is a property that possesses the essential attributes of ownership and transmissibility. The ownership in copyright is...
Background of the study
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ABSTRACT
The study centers on the “Use of Accounting as a Management Tool” (A case study of Anambra State Ag...
Background to the Study
Broadcast media thrived globally before the coming of the internet and new media as a very significant medium for...
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Background to the Study
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