Cluster analysis

Connectivity-based clustering, also known as hierarchical clustering, is based on the core idea of objects being more related to nearby objects than to objects farther away. These algorithms connect "objects" to form "clusters" based on their distance. A cluster can be described largely by the maximum distance needed to connect parts of the cluster. At different distances, different clusters will form, which can be represented using a dendrogram, which explains where the common name "hierarchical clustering" comes from: these algorithms do not provide a single partitioning of the data set, but instead provide an extensive hierarchy of clusters that merge with each other at certain distances. In a dendrogram, the y-axis marks the distance at which the clusters merge, while the objects are placed along the x-axis such that the clusters don't mix.

Connectivity-based clustering is a whole family of methods that differ by the way distances are computed. Apart from the usual choice of distance functions, the user also needs to decide on the linkage criterion (since a cluster consists of multiple objects, there are multiple candidates to compute the distance) to use. Popular choices are known as single-linkage clustering (the minimum of object distances), complete linkage clustering (the maximum of object distances), and UPGMA or WPGMA ("Unweighted or Weighted Pair Group Method with Arithmetic Mean", also known as average linkage clustering). Furthermore, hierarchical clustering can be agglomerative (starting with single elements and aggregating them into clusters) or divisive (starting with the complete data set and dividing it into partitions).

These methods will not produce a unique partitioning of the data set, but a hierarchy from which the user still needs to choose appropriate clusters. They are not very robust towards outliers, which will either show up as additional clusters or even cause other clusters to merge (known as "chaining phenomenon", in particular with single-linkage clustering). In the general case, the complexity is ${\displaystyle {\mathcal {O}}(n^{3})}$ for agglomerative clustering and ${\displaystyle {\mathcal {O}}(2^{n-1})}$ for divisive clustering,[7] which makes them too slow for large data sets. For some special cases, optimal efficient methods (of complexity ${\displaystyle {\mathcal {O}}(n^{2})}$) are known: SLINK[8] for single-linkage and CLINK[9] for complete-linkage clustering.

• Linkage clustering examples
• 
  Single-linkage on Gaussian data. At 35 clusters, the biggest cluster starts fragmenting into smaller parts, while before it was still connected to the second largest due to the single-link effect.
• 
  Single-linkage on density-based clusters. 20 clusters extracted, most of which contain single elements, since linkage clustering does not have a notion of "noise".

In centroid-based clustering, each cluster is represented by a central vector, which is not necessarily a member of the data set. When the number of clusters is fixed to k, k-means clustering gives a formal definition as an optimization problem: find the k cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized.

The optimization problem itself is known to be NP-hard, and thus the common approach is to search only for approximate solutions. A particularly well known approximate method is Lloyd's algorithm,[10] often just referred to as "k-means algorithm" (although another algorithm introduced this name). It does however only find a local optimum, and is commonly run multiple times with different random initializations. Variations of k-means often include such optimizations as choosing the best of multiple runs, but also restricting the centroids to members of the data set (k-medoids), choosing medians (k-medians clustering), choosing the initial centers less randomly (k-means++) or allowing a fuzzy cluster assignment (fuzzy c-means).

Most k-means-type algorithms require the number of clusters – k – to be specified in advance, which is considered to be one of the biggest drawbacks of these algorithms. Furthermore, the algorithms prefer clusters of approximately similar size, as they will always assign an object to the nearest centroid. This often leads to incorrectly cut borders of clusters (which is not surprising since the algorithm optimizes cluster centers, not cluster borders).

K-means has a number of interesting theoretical properties. First, it partitions the data space into a structure known as a Voronoi diagram. Second, it is conceptually close to nearest neighbor classification, and as such is popular in machine learning. Third, it can be seen as a variation of model based clustering, and Lloyd's algorithm as a variation of the Expectation-maximization algorithm for this model discussed below.

• k-means clustering examples
• 
  k-means separates data into Voronoi cells, which assumes equal-sized clusters (not adequate here).
• 
  k-means cannot represent density-based clusters.

Centroid-based clustering problems such as k-means and k-medoids are special cases of the uncapacitated, metric facility location problem, a canonical problem in the operations research and computational geometry communities. In a basic facility location problem (of which there are numerous variants that model more elaborate settings), the task is to find the best warehouse locations to optimally service a given set of consumers. One may view "warehouses" as cluster centroids and "consumer locations" as the data to be clustered. This makes it possible to apply the well-developed algorithmic solutions from the facility location literature to the presently considered centroid-based clustering problem.

The clustering model most closely related to statistics is based on distribution models. Clusters can then easily be defined as objects belonging most likely to the same distribution. A convenient property of this approach is that this closely resembles the way artificial data sets are generated: by sampling random objects from a distribution.

While the theoretical foundation of these methods is excellent, they suffer from overfitting unless constraints are put on the model complexity. A more complex model will usually be able to explain the data better, which makes choosing the appropriate model complexity inherently difficult.

One prominent method is known as Gaussian mixture models (using the expectation-maximization algorithm). Here, the data set is usually modeled with a fixed (to avoid overfitting) number of Gaussian distributions that are initialized randomly and whose parameters are iteratively optimized to better fit the data set. This will converge to a local optimum, so multiple runs may produce different results. In order to obtain a hard clustering, objects are often then assigned to the Gaussian distribution they most likely belong to; for soft clusterings, this is not necessary.

Distribution-based clustering produces complex models for clusters that can capture correlation and dependence between attributes. However, these algorithms put an extra burden on the user: for many real data sets, there may be no concisely defined mathematical model (e.g. assuming Gaussian distributions is a rather strong assumption on the data).

• Gaussian mixture model clustering examples
• 
  On Gaussian-distributed data, EM works well, since it uses Gaussians for modelling clusters.
• 
  Density-based clusters cannot be modeled using Gaussian distributions.

In density-based clustering,[11] clusters are defined as areas of higher density than the remainder of the data set. Objects in sparse areas – that are required to separate clusters – are usually considered to be noise and border points.

The most popular[12] density based clustering method is DBSCAN.[13] In contrast to many newer methods, it features a well-defined cluster model called "density-reachability". Similar to linkage based clustering, it is based on connecting points within certain distance thresholds. However, it only connects points that satisfy a density criterion, in the original variant defined as a minimum number of other objects within this radius. A cluster consists of all density-connected objects (which can form a cluster of an arbitrary shape, in contrast to many other methods) plus all objects that are within these objects' range. Another interesting property of DBSCAN is that its complexity is fairly low – it requires a linear number of range queries on the database – and that it will discover essentially the same results (it is deterministic for core and noise points, but not for border points) in each run, therefore there is no need to run it multiple times. OPTICS[14] is a generalization of DBSCAN that removes the need to choose an appropriate value for the range parameter ${\displaystyle \varepsilon }$, and produces a hierarchical result related to that of linkage clustering. DeLi-Clu,[15] Density-Link-Clustering combines ideas from single-linkage clustering and OPTICS, eliminating the ${\displaystyle \varepsilon }$ parameter entirely and offering performance improvements over OPTICS by using an R-tree index.

The key drawback of DBSCAN and OPTICS is that they expect some kind of density drop to detect cluster borders. On data sets with, for example, overlapping Gaussian distributions – a common use case in artificial data – the cluster borders produced by these algorithms will often look arbitrary, because the cluster density decreases continuously. On a data set consisting of mixtures of Gaussians, these algorithms are nearly always outperformed by methods such as EM clustering that are able to precisely model this kind of data.

Mean-shift is a clustering approach where each object is moved to the densest area in its vicinity, based on kernel density estimation. Eventually, objects converge to local maxima of density. Similar to k-means clustering, these "density attractors" can serve as representatives for the data set, but mean-shift can detect arbitrary-shaped clusters similar to DBSCAN. Due to the expensive iterative procedure and density estimation, mean-shift is usually slower than DBSCAN or k-Means. Besides that, the applicability of the mean-shift algorithm to multidimensional data is hindered by the unsmooth behaviour of the kernel density estimate, which results in over-fragmentation of cluster tails.[15]

• Density-based clustering examples
• 
  Density-based clustering with DBSCAN
• 
  DBSCAN assumes clusters of similar density, and may have problems separating nearby clusters.
• 
  OPTICS is a DBSCAN variant, improving handling of different densities clusters.

The grid-based technique is used for a multi-dimensional data set.[16] In this technique, we create a grid structure, and the comparison is performed on grids (also known as cells). The grid-based technique is fast and has low computational complexity. There are two types of grid-based clustering methods: STING and CLIQUE. Steps involved in grid-based clustering algorithm are:

1. Divide data space into a finite number of cells.
2. Randomly select a cell ‘c’, where c should not be traversed beforehand.
3. Calculate the density of ‘c’
4. If the density of ‘c’ greater than threshold density
   1. Mark cell ‘c’ as a new cluster
   2. Calculate the density of all the neighbors of ‘c’
   3. If the density of a neighboring cell is greater than threshold density then, add the cell in the cluster and repeat steps 4.2 and 4.3 till there is no neighbor with a density greater than threshold density.
5. Repeat steps 2,3 and 4 till all the cells are traversed.
6. Stop.

In recent years, considerable effort has been put into improving the performance of existing algorithms.[17][18] Among them are CLARANS,[19] and BIRCH.[20] With the recent need to process larger and larger data sets (also known as big data), the willingness to trade semantic meaning of the generated clusters for performance has been increasing. This led to the development of pre-clustering methods such as canopy clustering, which can process huge data sets efficiently, but the resulting "clusters" are merely a rough pre-partitioning of the data set to then analyze the partitions with existing slower methods such as k-means clustering.

For high-dimensional data, many of the existing methods fail due to the curse of dimensionality, which renders particular distance functions problematic in high-dimensional spaces. This led to new clustering algorithms for high-dimensional data that focus on subspace clustering (where only some attributes are used, and cluster models include the relevant attributes for the cluster) and correlation clustering that also looks for arbitrary rotated ("correlated") subspace clusters that can be modeled by giving a correlation of their attributes.[21] Examples for such clustering algorithms are CLIQUE[22] and SUBCLU.[23]

Ideas from density-based clustering methods (in particular the DBSCAN/OPTICS family of algorithms) have been adapted to subspace clustering (HiSC,[24] hierarchical subspace clustering and DiSH[25]) and correlation clustering (HiCO,[26] hierarchical correlation clustering, 4C[27] using "correlation connectivity" and ERiC[28] exploring hierarchical density-based correlation clusters).

Several different clustering systems based on mutual information have been proposed. One is Marina Meilă's variation of information metric;[29] another provides hierarchical clustering.[30] Using genetic algorithms, a wide range of different fit-functions can be optimized, including mutual information.[31] Also belief propagation, a recent development in computer science and statistical physics, has led to the creation of new types of clustering algorithms.[32]

When a clustering result is evaluated based on the data that was clustered itself, this is called internal evaluation. These methods usually assign the best score to the algorithm that produces clusters with high similarity within a cluster and low similarity between clusters. One drawback of using internal criteria in cluster evaluation is that high scores on an internal measure do not necessarily result in effective information retrieval applications.[36] Additionally, this evaluation is biased towards algorithms that use the same cluster model. For example, k-means clustering naturally optimizes object distances, and a distance-based internal criterion will likely overrate the resulting clustering.

Therefore, the internal evaluation measures are best suited to get some insight into situations where one algorithm performs better than another, but this shall not imply that one algorithm produces more valid results than another.[5] Validity as measured by such an index depends on the claim that this kind of structure exists in the data set. An algorithm designed for some kind of models has no chance if the data set contains a radically different set of models, or if the evaluation measures a radically different criterion.[5] For example, k-means clustering can only find convex clusters, and many evaluation indexes assume convex clusters. On a data set with non-convex clusters neither the use of k-means, nor of an evaluation criterion that assumes convexity, is sound.

More than a dozen of internal evaluation measures exist, usually based on the intuition that items in the same cluster should be more similar than items in different clusters.[37]^{: 115–121} For example, the following methods can be used to assess the quality of clustering algorithms based on internal criterion:

• Davies–Bouldin index

The Davies–Bouldin index can be calculated by the following formula:

${\displaystyle DB={\frac {1}{n}}\sum _{i=1}^{n}\max _{j\neq i}\left({\frac {\sigma _{i}+\sigma _{j}}{d(c_{i},c_{j})}}\right)}$

where n is the number of clusters, ${\displaystyle c_{i}}$ is the centroid of cluster ${\displaystyle i}$, ${\displaystyle \sigma _{i}}$ is the average distance of all elements in cluster ${\displaystyle i}$ to centroid ${\displaystyle c_{i}}$, and ${\displaystyle d(c_{i},c_{j})}$ is the distance between centroids ${\displaystyle c_{i}}$ and ${\displaystyle c_{j}}$. Since algorithms that produce clusters with low intra-cluster distances (high intra-cluster similarity) and high inter-cluster distances (low inter-cluster similarity) will have a low Davies–Bouldin index, the clustering algorithm that produces a collection of clusters with the smallest Davies–Bouldin index is considered the best algorithm based on this criterion.

• Dunn index

The Dunn index aims to identify dense and well-separated clusters. It is defined as the ratio between the minimal inter-cluster distance to maximal intra-cluster distance. For each cluster partition, the Dunn index can be calculated by the following formula:[38]

${\displaystyle D={\frac {\min _{1\leq i<j\leq n}d(i,j)}{\max _{1\leq k\leq n}d^{\prime }(k)}}\,,}$

where d(i,j) represents the distance between clusters i and j, and d '(k) measures the intra-cluster distance of cluster k. The inter-cluster distance d(i,j) between two clusters may be any number of distance measures, such as the distance between the centroids of the clusters. Similarly, the intra-cluster distance d '(k) may be measured in a variety ways, such as the maximal distance between any pair of elements in cluster k. Since internal criterion seek clusters with high intra-cluster similarity and low inter-cluster similarity, algorithms that produce clusters with high Dunn index are more desirable.

• Silhouette coefficient

The silhouette coefficient contrasts the average distance to elements in the same cluster with the average distance to elements in other clusters. Objects with a high silhouette value are considered well clustered, objects with a low value may be outliers. This index works well with k-means clustering, and is also used to determine the optimal number of clusters.

In external evaluation, clustering results are evaluated based on data that was not used for clustering, such as known class labels and external benchmarks. Such benchmarks consist of a set of pre-classified items, and these sets are often created by (expert) humans. Thus, the benchmark sets can be thought of as a gold standard for evaluation.[33] These types of evaluation methods measure how close the clustering is to the predetermined benchmark classes. However, it has recently been discussed whether this is adequate for real data, or only on synthetic data sets with a factual ground truth, since classes can contain internal structure, the attributes present may not allow separation of clusters or the classes may contain anomalies.[39] Additionally, from a knowledge discovery point of view, the reproduction of known knowledge may not necessarily be the intended result.[39] In the special scenario of constrained clustering, where meta information (such as class labels) is used already in the clustering process, the hold-out of information for evaluation purposes is non-trivial.[40]

A number of measures are adapted from variants used to evaluate classification tasks. In place of counting the number of times a class was correctly assigned to a single data point (known as true positives), such pair counting metrics assess whether each pair of data points that is truly in the same cluster is predicted to be in the same cluster.[33]

As with internal evaluation, several external evaluation measures exist,[37]^{: 125–129} for example:

• Purity: Purity is a measure of the extent to which clusters contain a single class.[36] Its calculation can be thought of as follows: For each cluster, count the number of data points from the most common class in said cluster. Now take the sum over all clusters and divide by the total number of data points. Formally, given some set of clusters ${\displaystyle M}$ and some set of classes ${\displaystyle D}$, both partitioning ${\displaystyle N}$ data points, purity can be defined as:

${\displaystyle {\frac {1}{N}}\sum _{m\in M}\max _{d\in D}{|m\cap d|}}$

This measure doesn't penalize having many clusters, and more clusters will make it easier to produce a high purity. A purity score of 1 is always possible by putting each data point in its own cluster. Also, purity doesn't work well for imbalanced data, where even poorly performing clustering algorithms will give a high purity value. For example, if a size 1000 dataset consists of two classes, one containing 999 points and the other containing 1 point, then every possible partition will have a purity of at least 99.9%.

• Rand index[41]

The Rand index computes how similar the clusters (returned by the clustering algorithm) are to the benchmark classifications. It can be computed using the following formula:

${\displaystyle RI={\frac {TP+TN}{TP+FP+FN+TN}}}$

where ${\displaystyle TP}$ is the number of true positives, ${\displaystyle TN}$ is the number of true negatives, ${\displaystyle FP}$ is the number of false positives, and ${\displaystyle FN}$ is the number of false negatives. The instances being counted here are the number of correct pairwise assignments. That is, ${\displaystyle TP}$ is the number of pairs of points that are clustered together in the predicted partition and in the ground truth partition, ${\displaystyle FP}$ is the number of pairs of points that are clustered together in the predicted partition but not in the ground truth partition etc. If the dataset is of size N, then ${\displaystyle TP+TN+FP+FN={\binom {N}{2}}}$.

One issue with the Rand index is that false positives and false negatives are equally weighted. This may be an undesirable characteristic for some clustering applications. The F-measure addresses this concern, as does the chance-corrected adjusted Rand index.

• F-measure

The F-measure can be used to balance the contribution of false negatives by weighting recall through a parameter ${\displaystyle \beta \geq 0}$. Let precision and recall (both external evaluation measures in themselves) be defined as follows:

${\displaystyle P={\frac {TP}{TP+FP}}}$

${\displaystyle R={\frac {TP}{TP+FN}}}$

where ${\displaystyle P}$ is the precision rate and ${\displaystyle R}$ is the recall rate. We can calculate the F-measure by using the following formula:[36]

${\displaystyle F_{\beta }={\frac {(\beta ^{2}+1)\cdot P\cdot R}{\beta ^{2}\cdot P+R}}}$

When ${\displaystyle \beta =0}$, ${\displaystyle F_{0}=P}$. In other words, recall has no impact on the F-measure when ${\displaystyle \beta =0}$, and increasing ${\displaystyle \beta }$ allocates an increasing amount of weight to recall in the final F-measure.

Also ${\displaystyle TN}$ is not taken into account and can vary from 0 upward without bound.

• Jaccard index

The Jaccard index is used to quantify the similarity between two datasets. The Jaccard index takes on a value between 0 and 1. An index of 1 means that the two dataset are identical, and an index of 0 indicates that the datasets have no common elements. The Jaccard index is defined by the following formula:

${\displaystyle J(A,B)={\frac {|A\cap B|}{|A\cup B|}}={\frac {TP}{TP+FP+FN}}}$

This is simply the number of unique elements common to both sets divided by the total number of unique elements in both sets.

Note that ${\displaystyle TN}$ is not taken into account.

• Dice index

The Dice symmetric measure doubles the weight on ${\displaystyle TP}$ while still ignoring ${\displaystyle TN}$:

${\displaystyle DSC={\frac {2TP}{2TP+FP+FN}}}$

• Fowlkes–Mallows index[42]

The Fowlkes–Mallows index computes the similarity between the clusters returned by the clustering algorithm and the benchmark classifications. The higher the value of the Fowlkes–Mallows index the more similar the clusters and the benchmark classifications are. It can be computed using the following formula:

${\displaystyle FM={\sqrt {{\frac {TP}{TP+FP}}\cdot {\frac {TP}{TP+FN}}}}}$

where ${\displaystyle TP}$ is the number of true positives, ${\displaystyle FP}$ is the number of false positives, and ${\displaystyle FN}$ is the number of false negatives. The ${\displaystyle FM}$ index is the geometric mean of the precision and recall ${\displaystyle P}$ and ${\displaystyle R}$, and is thus also known as the G-measure, while the F-measure is their harmonic mean.[43][44] Moreover, precision and recall are also known as Wallace's indices ${\displaystyle B^{I}}$ and ${\displaystyle B^{II}}$.[45] Chance normalized versions of recall, precision and G-measure correspond to Informedness, Markedness and Matthews Correlation and relate strongly to Kappa.[46]

• Chi Index[47] is a external validation index that measure the clustering results by applying the chi-squared statistic. This index scores positively the fact that the labels are as sparse as possible across the clusters, i.e., that each cluster has as few different labels as possible. The higher the value of the Chi Index the greater the relationship between the resulting clusters and the label used.
• The mutual information is an information theoretic measure of how much information is shared between a clustering and a ground-truth classification that can detect a non-linear similarity between two clusterings. Normalized mutual information is a family of corrected-for-chance variants of this that has a reduced bias for varying cluster numbers.[33]
• Confusion matrix

A confusion matrix can be used to quickly visualize the results of a classification (or clustering) algorithm. It shows how different a cluster is from the gold standard cluster.

To measure cluster tendency is to measure to what degree clusters exist in the data to be clustered, and may be performed as an initial test, before attempting clustering. One way to do this is to compare the data against random data. On average, random data should not have clusters.

• Hopkins statistic

There are multiple formulations of the Hopkins statistic.[48] A typical one is as follows.[49] Let ${\displaystyle X}$ be the set of ${\displaystyle n}$ data points in ${\displaystyle d}$ dimensional space. Consider a random sample (without replacement) of ${\displaystyle m\ll n}$ data points with members ${\displaystyle x_{i}}$. Also generate a set ${\displaystyle Y}$ of ${\displaystyle m}$ uniformly randomly distributed data points. Now define two distance measures, ${\displaystyle u_{i}}$ to be the distance of ${\displaystyle y_{i}\in Y}$ from its nearest neighbor in X and ${\displaystyle w_{i}}$ to be the distance of ${\displaystyle x_{i}\in X}$ from its nearest neighbor in X. We then define the Hopkins statistic as:

${\displaystyle H={\frac {\sum _{i=1}^{m}{u_{i}^{d}}}{\sum _{i=1}^{m}{u_{i}^{d}}+\sum _{i=1}^{m}{w_{i}^{d}}}}\,,}$

With this definition, uniform random data should tend to have values near to 0.5, and clustered data should tend to have values nearer to 1.

However, data containing just a single Gaussian will also score close to 1, as this statistic measures deviation from a uniform distribution, not multimodality, making this statistic largely useless in application (as real data never is remotely uniform).

Plant and animal ecology

Cluster analysis is used to describe and to make spatial and temporal comparisons of communities (assemblages) of organisms in heterogeneous environments. It is also used in plant systematics to generate artificial phylogenies or clusters of organisms (individuals) at the species, genus or higher level that share a number of attributes.

Transcriptomics

Clustering is used to build groups of genes with related expression patterns (also known as coexpressed genes) as in HCS clustering algorithm.[50][51] Often such groups contain functionally related proteins, such as enzymes for a specific pathway, or genes that are co-regulated. High throughput experiments using expressed sequence tags (ESTs) or DNA microarrays can be a powerful tool for genome annotation – a general aspect of genomics.

Sequence analysis

Sequence clustering is used to group homologous sequences into gene families.[52] This is a very important concept in bioinformatics, and evolutionary biology in general. See evolution by gene duplication.

High-throughput genotyping platforms

Clustering algorithms are used to automatically assign genotypes.[53]

Human genetic clustering

The similarity of genetic data is used in clustering to infer population structures.

Medical imaging

On PET scans, cluster analysis can be used to differentiate between different types of tissue in a three-dimensional image for many different purposes.[54]

Analysis of antimicrobial activity

Cluster analysis can be used to analyse patterns of antibiotic resistance, to classify antimicrobial compounds according to their mechanism of action, to classify antibiotics according to their antibacterial activity.

IMRT segmentation

Clustering can be used to divide a fluence map into distinct regions for conversion into deliverable fields in MLC-based Radiation Therapy.

Market research

Cluster analysis is widely used in market research when working with multivariate data from surveys and test panels. Market researchers use cluster analysis to partition the general population of consumers into market segments and to better understand the relationships between different groups of consumers/potential customers, and for use in market segmentation, product positioning, new product development and selecting test markets.

Grouping of shopping items

Clustering can be used to group all the shopping items available on the web into a set of unique products. For example, all the items on eBay can be grouped into unique products (eBay does not have the concept of a SKU).

Social network analysis

In the study of social networks, clustering may be used to recognize communities within large groups of people.

Search result grouping

In the process of intelligent grouping of the files and websites, clustering may be used to create a more relevant set of search results compared to normal search engines like Google. There are currently a number of web-based clustering tools such as Clusty. It also may be used to return a more comprehensive set of results in cases where a search term could refer to vastly different things. Each distinct use of the term corresponds to a unique cluster of results, allowing a ranking algorithm to return comprehensive results by picking the top result from each cluster.[55]

Slippy map optimization

Flickr's map of photos and other map sites use clustering to reduce the number of markers on a map. This makes it both faster and reduces the amount of visual clutter.

Software evolution

Clustering is useful in software evolution as it helps to reduce legacy properties in code by reforming functionality that has become dispersed. It is a form of restructuring and hence is a way of direct preventative maintenance.

Image segmentation

Clustering can be used to divide a digital image into distinct regions for border detection or object recognition.[56]

Evolutionary algorithms

Clustering may be used to identify different niches within the population of an evolutionary algorithm so that reproductive opportunity can be distributed more evenly amongst the evolving species or subspecies.

Recommender systems

Recommender systems are designed to recommend new items based on a user's tastes. They sometimes use clustering algorithms to predict a user's preferences based on the preferences of other users in the user's cluster.

Markov chain Monte Carlo methods

Clustering is often utilized to locate and characterize extrema in the target distribution.

Anomaly detection

Anomalies/outliers are typically – be it explicitly or implicitly – defined with respect to clustering structure in data.

Natural language processing

Clustering can be used to resolve lexical ambiguity.[55]

DevOps

Clustering has been used to analyse the effectiveness of DevOps teams.[57]

Sequence analysis in social sciences

Cluster analysis is used to identify patterns of family life trajectories, professional careers, and daily or weekly time use for example.

Crime analysis

Cluster analysis can be used to identify areas where there are greater incidences of particular types of crime. By identifying these distinct areas or "hot spots" where a similar crime has happened over a period of time, it is possible to manage law enforcement resources more effectively.

Educational data mining

Cluster analysis is for example used to identify groups of schools or students with similar properties.

Typologies

From poll data, projects such as those undertaken by the Pew Research Center use cluster analysis to discern typologies of opinions, habits, and demographics that may be useful in politics and marketing.

Field robotics

Clustering algorithms are used for robotic situational awareness to track objects and detect outliers in sensor data.[58]

Mathematical chemistry

To find structural similarity, etc., for example, 3000 chemical compounds were clustered in the space of 90 topological indices.[59]

Climatology

To find weather regimes or preferred sea level pressure atmospheric patterns.[60]

Finance

Cluster analysis has been used to cluster stocks into sectors.[61]

Petroleum geology

Cluster analysis is used to reconstruct missing bottom hole core data or missing log curves in order to evaluate reservoir properties.

Geochemistry

The clustering of chemical properties in different sample locations.

• Automatic clustering algorithms
• Balanced clustering
• Clustering high-dimensional data
• Conceptual clustering
• Consensus clustering
• Constrained clustering
• Community detection
• Data stream clustering
• HCS clustering
• Sequence clustering
• Spectral clustering

• Artificial neural network (ANN)
• Nearest neighbor search
• Neighbourhood components analysis
• Latent class analysis
• Affinity propagation

• Dimension reduction
• Principal component analysis
• Multidimensional scaling

• Cluster-weighted modeling
• Curse of dimensionality
• Determining the number of clusters in a data set
• Parallel coordinates
• Structured data analysis