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Accumulation point. In mathematics, a limit point, accumulation point, or cluster point of a set in a topological space is a point that can be "approximated" by points of in the sense that every neighbourhood of contains a point of other than itself. A limit point of a set does not itself have to be an element of There is also a closely related ...
Education in Kenya. Education in Kenya refers to the education system in Kenya. It is considered a basic right that should be offered to every individual. Education in Kenya predates to as early as the 18th century among the Swahili people. The earliest school was established by missionaries in Rabai.
In the theory of cluster analysis, the nearest-neighbor chain algorithm is an algorithm that can speed up several methods for agglomerative hierarchical clustering.These are methods that take a collection of points as input, and create a hierarchy of clusters of points by repeatedly merging pairs of smaller clusters to form larger clusters.
Determining the number of clusters in a data set, a quantity often labelled k as in the k -means algorithm, is a frequent problem in data clustering, and is a distinct issue from the process of actually solving the clustering problem. For a certain class of clustering algorithms (in particular k -means, k -medoids and expectation–maximization ...
Ordering points to identify the clustering structure ( OPTICS) is an algorithm for finding density-based [1] clusters in spatial data. It was presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander. [2] Its basic idea is similar to DBSCAN, [3] but it addresses one of DBSCAN's major weaknesses: the problem of ...
Joint Admission Board of Kenya (JAB) was the body that was mandated to conduct a joint admission exercise of students who have cleared secondary school into the Universities. The main aim was to ensure that only those who have attained a certain number of points are given priority to join the universities under a government sponsorship.
Automatic clustering algorithms. Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points. [1] [needs context]
to be the smallest (hence the operator in the formula) mean distance of i to all points in any other cluster (i.e., in any cluster of which i is not a member). The cluster with this smallest mean dissimilarity is said to be the "neighboring cluster" of i because it is the next best fit cluster for point i.