K-Means falls in the general category of clustering algorithms.
Data are a set of attributes on which the members of the population likely differ. The objective is classification. Here are some examples:
“How do competitors differ from one another on critical dimensions?”
“How is a particular market segmented?”
“Which dimensions are most important to differentiating between members of a population of interest?”
Source key:
The .hex key associated with the data set for use in clustering.
K
The desired number of clusters. There is no set rule or formula for defining K, it is up to the user and is often based on heuristics.
Max iter
The maximum number of iterations the algorithm is to go through if no stopping point is reached before then.
Initialization
- Plus Plus
A modification to the k-means algorithm that impacts the assignment of K initial cluster centroids. Because of the random process inherent to K-means, it’s possible for the algorithm to converge on centroids that are not the optimal cluster centers purely by chance in the choice of starting points. To mitigate this risk, Plus Plus initialization assigns K initial centers by choosing just one center at random, computing the Euclidean norm between that point and all other points in the data set, and using the results to define a weighted probability distribution from which the next center is chosen at random. The process repeats until all centers have been chosen, at which point the algorithm proceeds as usual.
- Furthest
- A modification to the k-means algorithm that impacts the assignment of K initial cluster centroids. Furthest first initialization attempts to improve K-means results by selecting the first center, and then calculating the distance from that point to all other possible points. The second initial center is chosen to the point furthest from the first center in terms of Euclidean distance.
Seed:
A means for specifying the algorithm components dependent on randomization. Note that the seed stays the same for each instance of H2O, allowing the user to create models with the same starting conditions in alternative configurations.
Normalize:
Specifies that each attribute be transformed such that it has a mean of 0 and standard deviation of 1, and that this transformation be carried out before the algorithm is applied.
Output from K-Means is a table with one more column than the number of attributes used to cluster. The the names of attributes, and “cluster” appear in the header row. The column cluster gives an arbitrary number to each cluster built, and the attributes give the coordinates of the center of that cluster.
| Clusters | Attribute 1 | Attribute 2 |
|---|---|---|
| 0 |
|
|
Xiong, Hui, Junjie Wu, and Jian Chen. “K-means Clustering Versus Validation Measures: A Data- distribution Perspective.” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 39.2 (2009): 318-331.
The number of clusters \(K\) is user defined and determined a priori.
Randomization
Choose \(K\) clusters from the set of \(N\) observations at random so that each observation has an equal chance of being chosen.
Plus Plus
Choose one center \(m_{1}\) at random.
Calculate the difference between \(m_{1}\) and each of the remaining \(N-1\) observations \(x_{i}\).
\(d(x_{i}, m_{1})\) = \(||(x_{i}-m_{1})||^2\)
Let \(P(i)\) be the probability of choosing \(x_{i}\) as \(m_{2}\). Weight \(P(i)\) by \(d(x_{i}, m_{1})\) so that those \(x_{i}\) furthest from \(m_{2}\) have a higher probability of being selected than those \(x_{i}\) close to \(m_{1}\).
Choose the next center \(m_{2}\) by drawing at random according to the weighted probability distribution.
Repeat until \(K\) centers have been chosen.
Furthest
Choose one center \(m_{1}\) at random.
Calculate the difference between \(m_{1}\) and each of the remaining \(N-1\) observations \(x_{i}\).
\(d(x_{i}, m_{1})\) = \(||(x_{i}-m_{1})||^2\)
Choose \(m_{2}\) to be the \(x_{i}\) that maximizes \(d(x_{i}, m_{1})\).
Repeat until \(K\) centers have been chosen.
Once \(K\) initial centers have been chosen calculate the difference between each observation \(x_{i}\) and each of the centers \(m_{1},...,m_{K}\), where difference is the squared Euclidean distance taken over \(p\) parameters.
\(d(x_{i}, m_{k})=\)
\(\sum_{j=1}^{p}(x_{ij}-m_{k})^2=\)
\(\lVert(x_{i}-m_{k})\rVert^2\)
Assign \(x_{i}\) to the cluster \(k\) defined by \(m_{k}\) that minimizes \(d(x_{i}, m_{k})\)
When all observations \(x_{i}\) are assigned to a cluster calculate the mean of the points in the cluster.
\(\bar{x}(k)=\lbrace\bar{x_{i1}},…\bar{x_{ip}}\rbrace\)
Set the \(\bar{x}(k)\) as the new cluster centers \(m_{k}\). Repeat steps 2 through 5 until the specified number of max iterations is reached or cluster assignments of the \(x_{i}\) are stable.
Hastie, Trevor, Robert Tibshirani, and J Jerome H Friedman. The Elements of Statistical Learning. Vol.1. N.p., Springer New York, 2001. http://www.stanford.edu/~hastie/local.ftp/Springer/OLD//ESLII_print4.pdf
Xiong, Hui, Junjie Wu, and Jian Chen. “K-means Clustering Versus Validation Measures: A Data- distribution Perspective.” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 39.2 (2009): 318-331.