Data clustering

From Helpful
Jump to: navigation, search
This is more for overview (my own) than for teaching or exercise.

For some more in-depth material to study, see e.g. the

Math on data:

some introduction · areas of statistics
types of data · on random variables, distributions
Virtues and shortcomings of...
on sampling · probability
glossary · references, unsorted
Footnotes on various analyses

Other data analysis, data summarization, learning

Data massage
Data clustering
Dimensionality reduction
Fuzzy coding, decisions, learning
Optimization theory, control theory
Connectionism, neural nets
Evolutionary computing


Clustering groups a few related sub-problems, including

cluster formation - organizing into clusters
cluster segmentation - dealing with boundaries (often using cluster centers)
labeling - assigning meaningful names
for the (relatively few) cases where this makes sense to estimate
deciding how many groups to have in the result
evaluation of a solution (possibly feeding back into the previous point)

Formally, the simplest clustering ca be described as:

  • you have a set of n data objects, call it D = { d1, ..., dn }
  • in its simplest shape, a clustering result is a disjoint partitioning of D
  • which makes clustering itself a function, mapping each datapoint to a cluster number/label that indicate membership of said cluster

The input data is often either

  • a set of a points in a many-dimensional space, usually a vector space, plus a metric to calculate distance between them, OR
  • a set of already-chosen distances
preferably complete, but depending on how it's made it might be sparse, and it may be easier to do some fuzzy statistical estimation than to ask people to complete it

The latter may well be in the form of a distance matrix / similarity matrix.

A bunch of methods given datapoints-plus-metric convert to that internally, but starting data-plus-metric is often a little more flexible up front - both for data massaging, and sometimes for implementation reasons.

That said, the choice of metric takes care, because there are many ways to accidentally put some bias into the metric.

Many methods look at element-to-element similarities/dissimilarities, while a few choose to be more involved with the data that comes from (e.g. some Maximum-likelihood-based methods common in bioinformatics)


Hard, soft, and fuzzy clustering

Hard clustering means each item should be assigned to a single group. This is essentially a partitioning.

Soft clustering means something can belong to more than one group.

Regularly used for data known to be too complex to be reduced cleanly with hard clustering, such as when there are closeby, overlapping, or ambiguous groups.

Soft clustering is generally understood as boolean soft clustering: something can belong to one or more clusters, but there are no degrees.

Fuzzy clustering is soft clustering plus degrees of membership.

This means intermediate results are effectively still moderately high-dimensional data, you often still have to make a decision about exclusion, thresholds or such (preferably within the algorithm, to have all information available).

If you don't make such a decision, the result more resembles dimensionality reduction.

Agglomerative versus divisive

Agglomerative clustering usually starts with each item in its own cluster and merges them where it seems a good idea.

Divisive clustering usually starts with every item in a single cluster and iteratively splits them as it sees fit.

The difference seems to lie largely in what side they err on in unclear cases.(verify)

Hierarchical clustering

Hierarchical clustering creates a tree of relations, often by an process where we keep tracks of how things join, rather than just assimilate things into a larger blob.

Hierarchical clusterers can be flexible, in that their results can partition into an arbitrary number of groups (by choosing the depth at which there are that amount of groups).

Depending on the data these results contain may also be useful as an approximation for fuzzy clustering. They may also be a little more helpful in cluster stability tests.

Some algorithms record and retain he distances of (/stress at) each such joint. These can be interesting to visualize (think dendrograms and such), and to effectively allow the amount-of-cluster choice to be made later (think threshold in a dendrogram).

Notes on....

Group number choice

Some algorithms try to decide on a suitable number of target groups, but many require you to choose an exact number before they get started.

This number is difficult to decide since there is usually is no well-defined, implicit, calculable best choice.

This is a problem particularly in hard clustering, because any decision of group membership is very final. The membership of bordercases may not be stable under even the slightest amount of (sample) noise.

Things you can do include:

  • use evaluation to measure the fitness of a solution (or sub-solutions while still clustering), based

Note that such a metric in itself is only a relative value in a distribution you don't know - you'll often have to calculate the fitness for many solutions to get a still-vague idea of fitness.

  • In the case of hard clustering, you can intentionally add some noise and see how much the membership of each item varies - and, say, report that as the confidence we have in a choice.
  • use some type of cross-validation

Inter-cluster and intra-cluster comparisons; susceptibilities

Depending on the algorithm, you often want to be able to compare

  • items to clusters (agglomerative and divisive decisions)
  • clusters to clusters (e.g. in hierarchical decisions)
  • items to items (e.g. for centroid/medoid decisions)

Cluster-to-cluster distances are most interesting to hierarchical clustering, and can be calculated in a number of ways (usually hardwired into the algorithm), including:

  • Single-link, a.k.a. minimum linkage or nearest linkage
the most similar combination (lowest distance) of possible comparisons
more susceptible to over-chaining than most other methods
  • Complete-link , a.k.a. max/farthest linkage
uses the least similar (largest distance) combination of possible comparisons
often gives a non-chained, more equally divided clusters than single-link
outliers may have disproportional influence
  • Average-link (a.k.a. group-average, a.k.a. Group-average (agglomerative) clustering (GAAC)) - average of distances between all inter-cluster pairs
less sensitive to outliers than complete-link, less sensitive to inversion than centroid approaches.
  • Centroid approaches use a calculated average for comparison to a cluster
quite susceptible to inversions (verify)
  • Medoid approaches try to use a representative item for comparison to a cluster
somewhat susceptible to inversions (verify)
  • Density-based methods care more about local density of items and less directly about the exact distances involved
  • Ward's method
based on Ward criterion, a.k.a. the Ward minimum variance criterion

Potential problems:

  • outliers
    • including a single outlier may drastically change comparisons to that group.
    • The timing of their inclustion can have significant effects on the result.
  • the chaining effect refers to algorithms doing a chain/string of assignments of to a group. The concept is clearest in
  • Inversions (sometimes 'reversal' or 'non-monotonicity') - describes when similarity values do not decrease monotonously in a series of iterations
    • easily happens when a process makes decisions based on centers that move in the process of clustering (such as in many centroid-style processes) particularly when combined with cases where there is no clear clustering solution.

See also:

TODO: read

on convergence
This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

Convergence is a nontrivial check in many algorithms.

You could check whether the group assignments have not changed, but this is sensitive to oscillations, resulting in a premature report of convergence and/or a failure to converge (depending somewhat on logic and data size).

A simple threshold is arbitrary since the error values often depend on the scale of the data values (which is not very trivial to correct for(verify)).

This is occasionally solved by error minimization criteria, for example minimization-of-the-sum-of-squares.

There are some details to this. For example, reallocating a point between clusters, various methods consider only the error decrease in the target cluster - while the solution's total error may increase. It usually still converges, but the total error decreases with a little more oscillation, which "no significant improvement in the last step" terminating criterion may be sensitive to (though arguably it's always more robust to check whether the error decrease is roughly asymptotic with the minimum you presume it'll get to).

The idea resembles Expectation Maximization (EM) methods in that it tries to maximize the probability of the clusters being the correct by minimizing the energy/error.

Purely random initial positioning may cause the local minimum problem. Smartly seeding the initial centroids helps and need not be too computationally expensive - and in fact helps convergence; see e.g. k-means++.

Alternatively, you could run many versions of the analysis, each with random initial placement, and see see whether (and/or to which degree) the results are stable, but this can be computationally expensive.

Robustness in hard clustering

Particularly hard clusterers are often not robust against even minor variations in the data. That is, separate data sets that are highly correlative may lead to significantly different results; areas in which membership is borderline flip-flip under the tiniest (sample) noise.

You can evaluating a solution for stress (or correlate distances it implies to the original data e.g. in hierarhical data), though in itself this is only a general thing. It is meaningful the same measre from other clusterings of the same data, meaning that you *can* roughly compare different solutions for expression of the original data, but only in a roughly converging way.

One trick is to cause the problem and test how varied results will be over mild variations over the data. You can for example repeat the clustering some amount of times with some noise, and record how often things change membership.

You can repeat the clustering omitting random pieces of data to lessen the effect of outliers - pieces of data that do not agree with the rest.

You can even aggregate the results from these runs and combine them into a sort of fuzzy cluster result that can show you instabilities, and/or converge on a clusters amount choice.

Silhouette coefficients
Davies-Bouldin index
Gap statistic

Implementation notes


The k-means problem is finding a cluster labelling for a given amount of clusters (k) with minimal error, where the error function is based on the the within-group sum of squares.

(For completeness, that means for all elements in a group, calculate the square of the euclidean distance to the centroid, and sum up all these squares, which gives per-cluster error values. Various convergence checks will want to know the sum of these errors)

Most implementations are iterative and look something like:

  • Position k cluster centroids (at random, or sometimes slightly more cleverly)
  • For each element, assign to the nearest centroid
  • Recalculate (affected) centroid means (and often the error/energy at the same time)
  • Check whether the moved centroids change the element assignments.
If so, iterate
If no change, we have converged and can stop

Of iterative clustering methods, k-means is the simplest and many others can be said to be based on it.

K-means gives better better results if the value for k is a good choice, representative for the data. (it's not unusual to try various k and test them)

The common 'nearest cluster' criterion will avoid attraction of multiple clusters, and the whole will converge to a decent solution for k groups.


  • results are sensitive to initial placement, and it is easy to get stuck in a local minimum.
It is not unusual to run the clustering various times with different starting clusters and see how stable the clustering is.
  • If k is not representative of the structure in the data the solution may not be satisfying at all. This is partly caused by, and partly independent of, the fact that there may be various possible stable clusterings.
  • the simple distance metric means we say the shape for inclusion is always a circle

Average-case runtime is decent because it's a fairly simple algorithm. Worst-case runtime, for fairly pathological datasets, is fairly quite high. There are faster approximations of k-means that you may wish to consider.

You can tweak k-means in various ways. For example, you can assign weights (based of frequency, importance, etc) to elements to affect the centroid mean recalculation.

See also:


  • ISODATA (Iterative Self-Organising Data Analysis Technique Algorithm) builds on k-means and tries to be smart in initial positions and in variations of k(verify).
  • H-means is a variation on k-means that recalculates the centroid only after a complete iteration over all the items, not after each reassignment.
Seen one way, it checks for error decrease less often, which makes it a smidge more sensitive to local minima and perhaps doesn't converge as nicely.
Although the difference in practice tends to be slight, k-means tends to be the slightly safer (and much more common) choice, even if the order it handles elements in is a different kind of bias that h-means avoids.
Canopy clustering

Bisecting k-means
This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

hard c-means
This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

UPGMA = Unweighed Pair Group Method using Arithmetic averaging

WPGMA = Weighed Pair Group Method using Arithmetic averaging

(These specific names/abbreviations come from Sneath and Sokal 1973)

UPGMA assigns equal weight to all distances:

D((u,v),w) = (nu*D(u,w) + nv*D(v,w)) / (nu+nu)

WPGMA uses:

D((u,v),w) = (nu*D(u,w) + nv*D(v,w)) / 2

In the unweighed variant, the two things being combined weigh equally, in the weighed variant, all leaves that are part of a cluster weigh in as much as the other part.

Bottom-up combiners working from a difference matrix, combining whatever leaf/cluster distance is minmal, then recalculating the difference matrix.

It's not too hard too argue this terminology is a little arbitrary

This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

Unweighed Pair Group Method using Centroids, and Weighed Pair Group Method using Centroids

(the specific names/abbreviations come from Sneath and Sokal 1973)

Fuzzy c-means (FCM)
This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)
  • Type: fuzzy clustering (not really partitioning anymore)

Method: Like k-means, but weighs centroid recentering calculations by fuzzy distance to all data points

Fuzzy k-means
This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

Shell clustering
This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

Basic fuzzy c-means would include by a radius - a sphere.

There are various other options, including:

  • fuzzy c-quadric shells algorithm (FCQS) detects ellipsoids
  • fuzzy c-varieties algorithm (FCV) detects infinite lines (linear manifolds) in 2D
  • adaptive fuzzy c-varieties algorithm (AFC): detects line segments in 2D data
  • fuzzy c-shells algorithm (FCS) detects circles
  • fuzzy c-spherical shells algorithm (FCSS) detects circles
  • fuzzy c-rings algorithm (FCR) detects circles
  • fuzzy c-rectangular shells algorithm (FCRS) detects rectangles
  • Gath-Geva algorithm (GG) detects ellipsoids
  • Gustafson-Kessel algorithm (GK) detects ellipsoids of roughly the same size

Partitional, medoid-based


Partitional, medoid-based



AGglomerative NESting



DIvisie ANAlysis





BITCH (balanced iterative reducing and clustering using hierarchies)



Clustering Using REpresentatives



"Rock: A robust clustering algorithm for categorical attributes"


Hybrid, Hierarchical

"CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling"



The assumption that real objects will always be a dense cloud of points more easily rejects random points as noise/outliers (even if relatively close).

It can also deal decently with closeby nonlinear clusters, if separated cleanly.


Similar to DBSCAN


Fuzzy, density-based

Clustering by Committee (CBC)


Based on responsive elements (a comittee) voting on specific outcomes.

See also:

Principal Direction Divisive Partitioning (PDDP)
Information Bottleneck

See also:

Agglomerative Information Bottleneck

See also:

Expectation Maximization Clustering

Related fields

See also