docs/site/algorithms-clustering.md - systemds - Git at Google

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 ## K-Means Clustering

 ### K-Means Description

 Given a collection of $n$ records with a pairwise similarity measure,
 the goal of clustering is to assign a category label to each record so
 that similar records tend to get the same label. In contrast to
 multinomial logistic regression, clustering is an *unsupervised*
 learning problem with neither category assignments nor label
 interpretations given in advance. In $k$-means clustering, the records
 $x_1, x_2, \ldots, x_n$ are numerical feature vectors of $\dim x_i = m$
 with the squared Euclidean distance $\|x_i - x_{i'}\|_2^2$ as the
 similarity measure. We want to partition $\\{x_1, \ldots, x_n\\}$ into $k$
 clusters $\\{S_1, \ldots, S_k\\}$ so that the aggregated squared distance
 from records to their cluster means is minimized:

 $$
 \begin{equation}
 \textrm{WCSS}\,\,=\,\, \sum_{i=1}^n \,\big\|x_i - mean(S_j: x_i\in S_j)\big\|_2^2 \,\,\to\,\,\min
 \end{equation}
 $$

 The aggregated distance measure in (1) is
 called the *within-cluster sum of squares* (WCSS). It can be viewed as a
 measure of residual variance that remains in the data after the
 clustering assignment, conceptually similar to the residual sum of
 squares (RSS) in linear regression. However, unlike for the RSS, the
 minimization of (1) is an NP-hard
 problem [[AloiseDHP2009]](algorithms-bibliography.html).

 Rather than searching for the global optimum in (1), a
 heuristic algorithm called Lloyd’s algorithm is typically used. This
 iterative algorithm maintains and updates a set of $k$ *centroids*
 $\\{c_1, \ldots, c_k\\}$, one centroid per cluster. It defines each
 cluster $S_j$ as the set of all records closer to $c_j$ than to any
 other centroid. Each iteration of the algorithm reduces the WCSS in two
 steps:

   1. Assign each record to the closest centroid, making
 $mean(S_j)\neq c_j$
   2. Reset each centroid to its cluster’s mean:
 $c_j := mean(S_j)$

 After Step 1, the centroids are generally
 different from the cluster means, so we can compute another
 "within-cluster sum of squares" based on the centroids:

 $$\textrm{WCSS_C}\,\,=\,\, \sum_{i=1}^n \,\big\|x_i - \mathop{\textrm{centroid}}(S_j: x_i\in S_j)\big\|_2^2
 \label{eqn:WCSS:C}$$

 This WCSS\_C after Step 1
 is less than the means-based WCSS before Step 1
 (or equal if convergence achieved), and in Step 2
 the WCSS cannot exceed the WCSS\_C for *the same* clustering; hence the
 WCSS reduction.

 Exact convergence is reached when each record becomes closer to its
 cluster’s mean than to any other cluster’s mean, so there are no more
 re-assignments and the centroids coincide with the means. In practice,
 iterations may be stopped when the reduction in WCSS (or in WCSS\_C)
 falls below a minimum threshold, or upon reaching the maximum number of
 iterations. The initialization of the centroids is also an important
 part of the algorithm. The smallest WCSS obtained by the algorithm is
 not the global minimum and varies depending on the initial centroids. We
 implement multiple parallel runs with different initial centroids and
 report the best result.

 **Scoring.** Our scoring script evaluates the clustering output by comparing it with
 a known category assignment. Since cluster labels have no prior
 correspondence to the categories, we cannot count "correct" and "wrong"
 cluster assignments. Instead, we quantify them in two ways:

   1. Count how many same-category and different-category pairs of records end
 up in the same cluster or in different clusters;
   2. For each category, count the prevalence of its most common cluster; for
 each cluster, count the prevalence of its most common category.

 The number of categories and the number of clusters ($k$) do not have to
 be equal. A same-category pair of records clustered into the same
 cluster is viewed as a "true positive," a different-category pair
 clustered together is a "false positive," a same-category pair clustered
 apart is a "false negative" etc.

 * * *

 #### Table 6

 The O-file for Kmeans-predict provides the
 output statistics in CSV format, one per line, in the following
 format: (NAME, \[CID\], VALUE). Note: the 1st group statistics are
 given if X input is available; the 2nd group statistics
 are given if X and C inputs are available;
 the 3rd and 4th group statistics are given if spY input
 is available; only the 4th group statistics contain a nonempty CID
 value; when present, CID contains either the specified category label
 or the predicted cluster label.

 <table>
   <thead>
     <tr>
       <th>Inputs Available</th>
       <th>Name</th>
       <th>CID</th>
       <th>Meaning</th>
     </tr>
   </thead>
   <tbody>
     <tr>
       <td style="text-align: center" rowspan="5">X</td>
       <td>TSS</td>
       <td>&#160;</td>
       <td>Total Sum of Squares (from the total mean)</td>
     </tr>
     <tr>
       <td>WCSS_M</td>
       <td>&#160;</td>
       <td>Within-Cluster Sum of Squares (means as centers)</td>
     </tr>
     <tr>
       <td>WCSS_M_PC</td>
       <td>&#160;</td>
       <td>Within-Cluster Sum of Squares (means), in % of TSS</td>
     </tr>
     <tr>
       <td>BCSS_M</td>
       <td>&#160;</td>
       <td>Between-Cluster Sum of Squares (means as centers)</td>
     </tr>
     <tr>
       <td>BCSS_M_PC</td>
       <td>&#160;</td>
       <td>Between-Cluster Sum of Squares (means), in % of TSS</td>
     </tr>
     <tr>
       <td style="text-align: center" rowspan="4">X and C</td>
       <td>WCSS_C</td>
       <td>&#160;</td>
       <td>Within-Cluster Sum of Squares (centroids as centers)</td>
     </tr>
     <tr>
       <td>WCSS_C_PC</td>
       <td>&#160;</td>
       <td>Within-Cluster Sum of Squares (centroids), % of TSS</td>
     </tr>
     <tr>
       <td>BCSS_C</td>
       <td>&#160;</td>
       <td>Between-Cluster Sum of Squares (centroids as centers)</td>
     </tr>
     <tr>
       <td>BCSS_C_PC</td>
       <td>&#160;</td>
       <td>Between-Cluster Sum of Squares (centroids), % of TSS</td>
     </tr>
     <tr>
       <td style="text-align: center" rowspan="8">spY</td>
       <td>TRUE_SAME_CT</td>
       <td>&#160;</td>
       <td>Same-category pairs predicted as Same-cluster, count</td>
     </tr>
     <tr>
       <td>TRUE_SAME_PC</td>
       <td>&#160;</td>
       <td>Same-category pairs predicted as Same-cluster, %</td>
     </tr>
     <tr>
       <td>TRUE_DIFF_CT</td>
       <td>&#160;</td>
       <td>Diff-category pairs predicted as Diff-cluster, count</td>
     </tr>
     <tr>
       <td>TRUE_DIFF_PC</td>
       <td>&#160;</td>
       <td>Diff-category pairs predicted as Diff-cluster, %</td>
     </tr>
     <tr>
       <td>FALSE_SAME_CT</td>
       <td>&#160;</td>
       <td>Diff-category pairs predicted as Same-cluster, count</td>
     </tr>
     <tr>
       <td>FALSE_SAME_PC</td>
       <td>&#160;</td>
       <td>Diff-category pairs predicted as Same-cluster, %</td>
     </tr>
     <tr>
       <td>FALSE_DIFF_CT</td>
       <td>&#160;</td>
       <td>Same-category pairs predicted as Diff-cluster, count</td>
     </tr>
     <tr>
       <td>FALSE_DIFF_PC</td>
       <td>&#160;</td>
       <td>Same-category pairs predicted as Diff-cluster, %</td>
     </tr>
     <tr>
       <td style="text-align: center" rowspan="8">spY</td>
       <td>SPEC_TO_PRED</td>
       <td style="text-align: center">+</td>
       <td>For specified category, the best predicted cluster id</td>
     </tr>
     <tr>
       <td>SPEC_FULL_CT</td>
       <td style="text-align: center">+</td>
       <td>For specified category, its full count</td>
     </tr>
     <tr>
       <td>SPEC_MATCH_CT</td>
       <td style="text-align: center">+</td>
       <td>For specified category, best-cluster matching count</td>
     </tr>
     <tr>
       <td>SPEC_MATCH_PC</td>
       <td style="text-align: center">+</td>
       <td>For specified category, % of matching to full count</td>
     </tr>
     <tr>
       <td>PRED_TO_SPEC</td>
       <td style="text-align: center">+</td>
       <td>For predicted cluster, the best specified category id</td>
     </tr>
     <tr>
       <td>PRED_FULL_CT</td>
       <td style="text-align: center">+</td>
       <td>For predicted cluster, its full count</td>
     </tr>
     <tr>
       <td>PRED_MATCH_CT</td>
       <td style="text-align: center">+</td>
       <td>For predicted cluster, best-category matching count</td>
     </tr>
     <tr>
       <td>PRED_MATCH_PC</td>
       <td style="text-align: center">+</td>
       <td>For predicted cluster, % of matching to full count</td>
     </tr>
   </tbody>
 </table>

 * * *

 ### K-Means Details

 Our clustering script proceeds in 3 stages: centroid initialization,
 parallel $k$-means iterations, and the best-available output generation.
 Centroids are initialized at random from the input records (the rows
 of $X$), biased towards being chosen far apart from each other. The
 initialization method is based on the `k-means++` heuristic
 from [[ArthurVassilvitskii2007]](algorithms-bibliography.html), with one important difference: to
 reduce the number of passes through $X$, we take a small sample of $X$
 and run the `k-means++` heuristic over this sample. Here is,
 conceptually, our centroid initialization algorithm for one clustering
 run:

 1. Sample the rows of $X$ uniformly at random, picking each row with
 probability $p = ks / n$ where
    - $k$ is the number of centroids
    - $n$ is the number of records
    - $s$ is the samp input parameter

    If $ks \geq n$, the entire $X$ is used in place of its sample.

 2. Choose the first centroid uniformly at random from the sampled rows.
 3. Choose each subsequent centroid from the sampled rows, at random, with
 probability proportional to the squared Euclidean distance between the
 row and the nearest already-chosen centroid.

 The sampling of $X$ and the selection of centroids are performed
 independently and in parallel for each run of the $k$-means algorithm.
 When we sample the rows of $X$, rather than tossing a random coin for
 each row, we compute the number of rows to skip until the next sampled
 row as $\lceil \log(u) / \log(1 - p) \rceil$ where $u\in (0, 1)$ is
 uniformly random. This time-saving trick works because

 $$Prob[k-1 < \log_{1-p}(u) < k] \,\,=\,\, p(1-p)^{k-1} \,\,=\,\,
 Prob[\textrm{skip $k-1$ rows}]$$

 However, it requires us to estimate the maximum sample size, which we
 set near $ks + 10\sqrt{ks}$ to make it generous enough.

 Once we selected the initial centroid sets, we start the $k$-means
 iterations independently in parallel for all clustering runs. The number
 of clustering runs is given as the runs input parameter.
 Each iteration of each clustering run performs the following steps:

 - Compute the centroid-dependent part of squared Euclidean distances from
 all records (rows of $X$) to each of the $k$ centroids using matrix
 product.
 - Take the minimum of the above for each record.
 - Update the current within-cluster sum of squares (WCSS) value, with
 centroids substituted instead of the means for efficiency.
 - Check the convergence

    criterion:

    $$\textrm{WCSS}_{\mathrm{old}} - \textrm{WCSS}_{\mathrm{new}} < {\varepsilon}\cdot\textrm{WCSS}_{\mathrm{new}}$$

    as well as the number of iterations limit.
 - Find the closest centroid for each record, sharing equally any records with multiple closest centroids.
 - Compute the number of records closest to each centroid, checking for
 "runaway" centroids with no records left (in which case the run fails).
 - Compute the new centroids by averaging the records in their clusters.

 When a termination condition is satisfied, we store the centroids and
 the WCSS value and exit this run. A run has to satisfy the WCSS
 convergence criterion to be considered successful. Upon the termination
 of all runs, we select the smallest WCSS value among the successful
 runs, and write out this run’s centroids. If requested, we also compute
 the cluster assignment of all records in $X$, using integers from 1
 to $k$ as the cluster labels. The scoring script can then be used to
 compare the cluster assignment with an externally specified category
 assignment.

 ### Returns

 We output the $k$ centroids for the best available clustering,
 i. e. whose WCSS is the smallest of all successful runs. The centroids
 are written as the rows of the $k\,{\times}\,m$-matrix into the output
 file whose path/name was provided as the `C` input
 argument. If the input parameter `isY` was set
 to `1`, we also output the one-column matrix with the cluster
 assignment for all the records. This assignment is written into the file
 whose path/name was provided as the `Y` input argument. The
 best WCSS value, as well as some information about the performance of
 the other runs, is printed during the script execution. The scoring
 script `Kmeans-predict.dml` prints all its results in a
 self-explanatory manner, as defined in
 [**Table 6**](#table-6).
	---
	layout: site
	title: Algorithms Reference Clustering
	---
	<!--
	{% comment %}
	Licensed to the Apache Software Foundation (ASF) under one or more
	contributor license agreements. See the NOTICE file distributed with
	this work for additional information regarding copyright ownership.
	The ASF licenses this file to you under the Apache License, Version 2.0
	(the "License"); you may not use this file except in compliance with
	the License. You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License.
	{% endcomment %}
	-->

	## K-Means Clustering

	### K-Means Description

	Given a collection of $n$ records with a pairwise similarity measure,
	the goal of clustering is to assign a category label to each record so
	that similar records tend to get the same label. In contrast to
	multinomial logistic regression, clustering is an unsupervised
	learning problem with neither category assignments nor label
	interpretations given in advance. In $k$-means clustering, the records
	$x_1, x_2, \ldots, x_n$ are numerical feature vectors of $\dim x_i = m$
	with the squared Euclidean distance $\\|x_i - x_{i'}\\|_2^2$ as the
	similarity measure. We want to partition $\\{x_1, \ldots, x_n\\}$ into $k$
	clusters $\\{S_1, \ldots, S_k\\}$ so that the aggregated squared distance
	from records to their cluster means is minimized:

	$$
	\begin{equation}
	\textrm{WCSS}\,\,=\,\, \sum_{i=1}^n \,\big\\|x_i - mean(S_j: x_i\in S_j)\big\\|_2^2 \,\,\to\,\,\min
	\end{equation}
	$$

	The aggregated distance measure in (1) is
	called the within-cluster sum of squares (WCSS). It can be viewed as a
	measure of residual variance that remains in the data after the
	clustering assignment, conceptually similar to the residual sum of
	squares (RSS) in linear regression. However, unlike for the RSS, the
	minimization of (1) is an NP-hard
	problem [[AloiseDHP2009]](algorithms-bibliography.html).

	Rather than searching for the global optimum in (1), a
	heuristic algorithm called Lloyd’s algorithm is typically used. This
	iterative algorithm maintains and updates a set of $k$ centroids
	$\\{c_1, \ldots, c_k\\}$, one centroid per cluster. It defines each
	cluster $S_j$ as the set of all records closer to $c_j$ than to any
	other centroid. Each iteration of the algorithm reduces the WCSS in two
	steps:

	1. Assign each record to the closest centroid, making
	$mean(S_j)\neq c_j$
	2. Reset each centroid to its cluster’s mean:
	$c_j := mean(S_j)$

	After Step 1, the centroids are generally
	different from the cluster means, so we can compute another
	"within-cluster sum of squares" based on the centroids:

	$$\textrm{WCSS_C}\,\,=\,\, \sum_{i=1}^n \,\big\\|x_i - \mathop{\textrm{centroid}}(S_j: x_i\in S_j)\big\\|_2^2
	\label{eqn:WCSS:C}$$

	This WCSS\_C after Step 1
	is less than the means-based WCSS before Step 1
	(or equal if convergence achieved), and in Step 2
	the WCSS cannot exceed the WCSS\_C for the same clustering; hence the
	WCSS reduction.

	Exact convergence is reached when each record becomes closer to its
	cluster’s mean than to any other cluster’s mean, so there are no more
	re-assignments and the centroids coincide with the means. In practice,
	iterations may be stopped when the reduction in WCSS (or in WCSS\_C)
	falls below a minimum threshold, or upon reaching the maximum number of
	iterations. The initialization of the centroids is also an important
	part of the algorithm. The smallest WCSS obtained by the algorithm is
	not the global minimum and varies depending on the initial centroids. We
	implement multiple parallel runs with different initial centroids and
	report the best result.

	Scoring. Our scoring script evaluates the clustering output by comparing it with
	a known category assignment. Since cluster labels have no prior
	correspondence to the categories, we cannot count "correct" and "wrong"
	cluster assignments. Instead, we quantify them in two ways:

	1. Count how many same-category and different-category pairs of records end
	up in the same cluster or in different clusters;
	2. For each category, count the prevalence of its most common cluster; for
	each cluster, count the prevalence of its most common category.

	The number of categories and the number of clusters ($k$) do not have to
	be equal. A same-category pair of records clustered into the same
	cluster is viewed as a "true positive," a different-category pair
	clustered together is a "false positive," a same-category pair clustered
	apart is a "false negative" etc.

	* * *

	#### Table 6

	The O-file for Kmeans-predict provides the
	output statistics in CSV format, one per line, in the following
	format: (NAME, \[CID\], VALUE). Note: the 1st group statistics are
	given if X input is available; the 2nd group statistics
	are given if X and C inputs are available;
	the 3rd and 4th group statistics are given if spY input
	is available; only the 4th group statistics contain a nonempty CID
	value; when present, CID contains either the specified category label
	or the predicted cluster label.

	<table>
	<thead>
	<tr>
	<th>Inputs Available</th>
	<th>Name</th>
	<th>CID</th>
	<th>Meaning</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td style="text-align: center" rowspan="5">X</td>
	<td>TSS</td>
	<td> </td>
	<td>Total Sum of Squares (from the total mean)</td>
	</tr>
	<tr>
	<td>WCSS_M</td>
	<td> </td>
	<td>Within-Cluster Sum of Squares (means as centers)</td>
	</tr>
	<tr>
	<td>WCSS_M_PC</td>
	<td> </td>
	<td>Within-Cluster Sum of Squares (means), in % of TSS</td>
	</tr>
	<tr>
	<td>BCSS_M</td>
	<td> </td>
	<td>Between-Cluster Sum of Squares (means as centers)</td>
	</tr>
	<tr>
	<td>BCSS_M_PC</td>
	<td> </td>
	<td>Between-Cluster Sum of Squares (means), in % of TSS</td>
	</tr>
	<tr>
	<td style="text-align: center" rowspan="4">X and C</td>
	<td>WCSS_C</td>
	<td> </td>
	<td>Within-Cluster Sum of Squares (centroids as centers)</td>
	</tr>
	<tr>
	<td>WCSS_C_PC</td>
	<td> </td>
	<td>Within-Cluster Sum of Squares (centroids), % of TSS</td>
	</tr>
	<tr>
	<td>BCSS_C</td>
	<td> </td>
	<td>Between-Cluster Sum of Squares (centroids as centers)</td>
	</tr>
	<tr>
	<td>BCSS_C_PC</td>
	<td> </td>
	<td>Between-Cluster Sum of Squares (centroids), % of TSS</td>
	</tr>
	<tr>
	<td style="text-align: center" rowspan="8">spY</td>
	<td>TRUE_SAME_CT</td>
	<td> </td>
	<td>Same-category pairs predicted as Same-cluster, count</td>
	</tr>
	<tr>
	<td>TRUE_SAME_PC</td>
	<td> </td>
	<td>Same-category pairs predicted as Same-cluster, %</td>
	</tr>
	<tr>
	<td>TRUE_DIFF_CT</td>
	<td> </td>
	<td>Diff-category pairs predicted as Diff-cluster, count</td>
	</tr>
	<tr>
	<td>TRUE_DIFF_PC</td>
	<td> </td>
	<td>Diff-category pairs predicted as Diff-cluster, %</td>
	</tr>
	<tr>
	<td>FALSE_SAME_CT</td>
	<td> </td>
	<td>Diff-category pairs predicted as Same-cluster, count</td>
	</tr>
	<tr>
	<td>FALSE_SAME_PC</td>
	<td> </td>
	<td>Diff-category pairs predicted as Same-cluster, %</td>
	</tr>
	<tr>
	<td>FALSE_DIFF_CT</td>
	<td> </td>
	<td>Same-category pairs predicted as Diff-cluster, count</td>
	</tr>
	<tr>
	<td>FALSE_DIFF_PC</td>
	<td> </td>
	<td>Same-category pairs predicted as Diff-cluster, %</td>
	</tr>
	<tr>
	<td style="text-align: center" rowspan="8">spY</td>
	<td>SPEC_TO_PRED</td>
	<td style="text-align: center">+</td>
	<td>For specified category, the best predicted cluster id</td>
	</tr>
	<tr>
	<td>SPEC_FULL_CT</td>
	<td style="text-align: center">+</td>
	<td>For specified category, its full count</td>
	</tr>
	<tr>
	<td>SPEC_MATCH_CT</td>
	<td style="text-align: center">+</td>
	<td>For specified category, best-cluster matching count</td>
	</tr>
	<tr>
	<td>SPEC_MATCH_PC</td>
	<td style="text-align: center">+</td>
	<td>For specified category, % of matching to full count</td>
	</tr>
	<tr>
	<td>PRED_TO_SPEC</td>
	<td style="text-align: center">+</td>
	<td>For predicted cluster, the best specified category id</td>
	</tr>
	<tr>
	<td>PRED_FULL_CT</td>
	<td style="text-align: center">+</td>
	<td>For predicted cluster, its full count</td>
	</tr>
	<tr>
	<td>PRED_MATCH_CT</td>
	<td style="text-align: center">+</td>
	<td>For predicted cluster, best-category matching count</td>
	</tr>
	<tr>
	<td>PRED_MATCH_PC</td>
	<td style="text-align: center">+</td>
	<td>For predicted cluster, % of matching to full count</td>
	</tr>
	</tbody>
	</table>

	* * *

	### K-Means Details

	Our clustering script proceeds in 3 stages: centroid initialization,
	parallel $k$-means iterations, and the best-available output generation.
	Centroids are initialized at random from the input records (the rows
	of $X$), biased towards being chosen far apart from each other. The
	initialization method is based on the `k-means++` heuristic
	from [[ArthurVassilvitskii2007]](algorithms-bibliography.html), with one important difference: to
	reduce the number of passes through $X$, we take a small sample of $X$
	and run the `k-means++` heuristic over this sample. Here is,
	conceptually, our centroid initialization algorithm for one clustering
	run:

	1. Sample the rows of $X$ uniformly at random, picking each row with
	probability $p = ks / n$ where
	- $k$ is the number of centroids
	- $n$ is the number of records
	- $s$ is the samp input parameter

	If $ks \geq n$, the entire $X$ is used in place of its sample.

	2. Choose the first centroid uniformly at random from the sampled rows.
	3. Choose each subsequent centroid from the sampled rows, at random, with
	probability proportional to the squared Euclidean distance between the
	row and the nearest already-chosen centroid.

	The sampling of $X$ and the selection of centroids are performed
	independently and in parallel for each run of the $k$-means algorithm.
	When we sample the rows of $X$, rather than tossing a random coin for
	each row, we compute the number of rows to skip until the next sampled
	row as $\lceil \log(u) / \log(1 - p) \rceil$ where $u\in (0, 1)$ is
	uniformly random. This time-saving trick works because

	$$Prob[k-1 < \log_{1-p}(u) < k] \,\,=\,\, p(1-p)^{k-1} \,\,=\,\,
	Prob[\textrm{skip $k-1$ rows}]$$

	However, it requires us to estimate the maximum sample size, which we
	set near $ks + 10\sqrt{ks}$ to make it generous enough.

	Once we selected the initial centroid sets, we start the $k$-means
	iterations independently in parallel for all clustering runs. The number
	of clustering runs is given as the runs input parameter.
	Each iteration of each clustering run performs the following steps:

	- Compute the centroid-dependent part of squared Euclidean distances from
	all records (rows of $X$) to each of the $k$ centroids using matrix
	product.
	- Take the minimum of the above for each record.
	- Update the current within-cluster sum of squares (WCSS) value, with
	centroids substituted instead of the means for efficiency.
	- Check the convergence

	criterion:

	$$\textrm{WCSS}_{\mathrm{old}} - \textrm{WCSS}_{\mathrm{new}} < {\varepsilon}\cdot\textrm{WCSS}_{\mathrm{new}}$$

	as well as the number of iterations limit.
	- Find the closest centroid for each record, sharing equally any records with multiple closest centroids.
	- Compute the number of records closest to each centroid, checking for
	"runaway" centroids with no records left (in which case the run fails).
	- Compute the new centroids by averaging the records in their clusters.

	When a termination condition is satisfied, we store the centroids and
	the WCSS value and exit this run. A run has to satisfy the WCSS
	convergence criterion to be considered successful. Upon the termination
	of all runs, we select the smallest WCSS value among the successful
	runs, and write out this run’s centroids. If requested, we also compute
	the cluster assignment of all records in $X$, using integers from 1
	to $k$ as the cluster labels. The scoring script can then be used to
	compare the cluster assignment with an externally specified category
	assignment.

	### Returns

	We output the $k$ centroids for the best available clustering,
	i. e. whose WCSS is the smallest of all successful runs. The centroids
	are written as the rows of the $k\,{\times}\,m$-matrix into the output
	file whose path/name was provided as the `C` input
	argument. If the input parameter `isY` was set
	to `1`, we also output the one-column matrix with the cluster
	assignment for all the records. This assignment is written into the file
	whose path/name was provided as the `Y` input argument. The
	best WCSS value, as well as some information about the performance of
	the other runs, is printed during the script execution. The scoring
	script `Kmeans-predict.dml` prints all its results in a
	self-explanatory manner, as defined in
	[Table 6](#table-6).