Cluster analysis and K-means++

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between Cluster analysis and K-means++

Cluster analysis vs. K-means++

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). In data mining, k-means++ is an algorithm for choosing the initial values (or "seeds") for the ''k''-means clustering algorithm.

Similarities between Cluster analysis and K-means++

Cluster analysis and K-means++ have 4 things in common (in Unionpedia): Data mining, K-means clustering, Lloyd's algorithm, NP-hardness.

Data mining

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Cluster analysis and Data mining · Data mining and K-means++ · See more »

K-means clustering

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining.

Cluster analysis and K-means clustering · K-means clustering and K-means++ · See more »

Lloyd's algorithm

In computer science and electrical engineering, Lloyd's algorithm, also known as Voronoi iteration or relaxation, is an algorithm named after Stuart P. Lloyd for finding evenly spaced sets of points in subsets of Euclidean spaces and partitions of these subsets into well-shaped and uniformly sized convex cells.

Cluster analysis and Lloyd's algorithm · K-means++ and Lloyd's algorithm · See more »

NP-hardness

NP-hardness (''n''on-deterministic ''p''olynomial-time hardness), in computational complexity theory, is the defining property of a class of problems that are, informally, "at least as hard as the hardest problems in NP".

Cluster analysis and NP-hardness · K-means++ and NP-hardness · See more »

The list above answers the following questions

What Cluster analysis and K-means++ have in common
What are the similarities between Cluster analysis and K-means++

Cluster analysis and K-means++ Comparison

Cluster analysis has 169 relations, while K-means++ has 14. As they have in common 4, the Jaccard index is 2.19% = 4 / (169 + 14).

References

This article shows the relationship between Cluster analysis and K-means++. To access each article from which the information was extracted, please visit:

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