Examples of 'k-means algorithm' in a sentence
Meaning of "k-means algorithm"
k-means algorithm - K-means algorithm is a popular clustering algorithm used in machine learning. It is used to partition a set of data points into k clusters based on their features and similarities
How to use "k-means algorithm" in a sentence
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k-means algorithm
Spark MLlib implements a distributed k-means algorithm.
The K-Means algorithm clusters sequences into families.
Cluster your data using the k-means algorithm.
The k-means algorithm partitions the dataset into k clusters.
One of the possible techniques is based on the k-means algorithm.
The k-means algorithm to optimize the codebook entries according to the source distribution.
This dissertation presents a novel approach for incorporating semi-supervision in the wellknown k-means algorithm.
We used the k-means algorithm to divide the data into two and three groups.
The k-medoids algorithm is a clustering algorithm related to the k-means algorithm and the medoidshift algorithm.
The K-means algorithm is then applied to identify regions.
According to a preferred embodiment, the classification method is the K-means algorithm.
A parallel implementation of the k-means algorithm is presented with application to 3D data.
The intervals can also be determined by using a clustering algorithm, such as the K-means algorithm.
The starting point of the K-means algorithm is to initiate k randomly generated seed clusters.
The method according to claim 1, wherein the clustering algorithm comprises the K-Means algorithm.
See also
Call k-means algorithm to position K centroids.
The other algorithm is developed using the K-means algorithm and its variants.
The k-means algorithm follows sequence of steps,.
The major problems of the K-means algorithm are that,.
You argue that the k-means algorithm will work fine on non-spherical clusters.
Dimension reduction, approximate nearest neighbor search, the k-means algorithm ( F. Chazal ).
Centroid models, for example, the k-means algorithm represents each cluster by a single mean vector.
Finally, the K-means algorithm is applied to the ACM results.
Clustering techniques include the MacQueen 's K-means algorithm and the Kohonen 's Self-Organizing Map algorithm.
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K-means has a number of interesting theoretical properties
A key limitation of k-means is its cluster model
K-means clustering is an approach for vector quantization
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