# Tagged: clustering

# Change Detection Tutorial

I’ve been working on a tutorial on change detection. This is the first time I’ve attempted to write a tutorial, and it’s been a useful learning process. I’m not “done” yet, but I feel it is at the point where I can announce that it exists.

While the transition from “notebooks where Aman is fooling around” to “a well-written tutorial with a narrative” is far from complete, I’ve invested enough time without any validation of whether anyone is actually interested in reading all this. If there’s significant interest or requests, I will be happy to invest more time in cleaning up this tutorial and maybe even adding more topics.

You can check out my tutorial on change detection here:

https://github.com/amanahuja/change-detection-tutorial

## Why a tutorial on change detection?

Change detection is a type of problem in which we want to detect an interesting change in a signal. In the tutorial, I consider the signal to be a stream of (scalar) values, and take an online approach to the problem. I leave it open to interpretation what we mean by “interesting change”.

The objective of the tutorial is to introduce to discuss some fundamental concepts in change detection, to highlight some important use-cases of these algorithms, and to encourage the reader to think about the context of the problem before designing a solution.

I find this topic very interesting, and I’ve been fortunate to have had the chance to work on a few projects over the last years in which change detection was an important component. The problem is also tied to the more general subject of anomaly detection, which is turning out to be a recurring theme in my work.

Most importantly, I think there is a huge and growing demand in this subject. It is simply impossible to use purely manual methods to keep tabs on the tremendous amounts of data that we’re generating as a society, and in many scenarios the most interesting things are those that are NOT normal — they that fall rapidly, they rise sharply, they fit an unusual pattern or or they do not fit a usual pattern. Systems that utilize change detection algorithms — often as part of a larger solution — will help us sort through all our data and enable appropriate decisions to be made accurately and on time.

## Topics Covered

Some of the topics covered in the tutorial are:

- Online vs Offline algorithms, simulating online detection.
- Designing residuals and stop conditions
- Welford’s method
- Comparing multiple windows on a single stream.
- Anomaly detection in EKG signals

## Some screenshots

## EKG signals

To look at EKG signals, I borrowed from Ted Dunning’s presentation at Strata. I recreated his solution in python (his solution, available on github at https://github.com/tdunning/anomaly-detection/ uses Java and Mahout).

I actually haven’t finished writing this section yet, it’s an exciting enough topic that I feel I could sink a lot of time into it. I spent (only) several hours reading and ingesting the EKG data, and (only) several more hours re-writing Ted Dunning’s algorithm. But after that initial effort, I put in a large amount of intermittent effort trying to make this section presentable and useful for a tutorial — and therein lies the time sink. I’ll fix this section up based on feedback from readers.

Ted Dunning’s approach to anomaly detection in EKG signals is as follows. First, a dictionary of representative small segments (windows) is created. These windows are found by using k-means clustering on a “normal” EKG signal. This dictionary, constructed from the training data, is used to reconstruct the EKG signal of interest.

If a test signal is successfully reconstructed from the dictionary, the signal is much like those found in the training data, and can be considered normal. If the reconstruction has a high error rate, there’s something in the signal that may be anomalous, suggesting a potentially unhealthy EKG that should be investigated further.

I have not tuned the model in the tutorial; there is room for improvement in the training phase, parameter selection, and other adjustments. That’s another potential time sink, so I’ve temporarily convinced myself that it’s actually better, for a tutorial, to leave that as an exercise for the reader.

If all this sounds interesting, please do take a look at the tutorial here:

https://github.com/amanahuja/change-detection-tutorial

I’ll be happy to receive any comments or feedback!

# Non-convex sets with k-means and hierarchical clustering

## Bad mouthing old friends

I got into a conversation recently about k-means clustering — you know, as you do — and let me tell you, poor k-means was really getting bashed. “K-means sucks at this”, “K-means can’t do that”. It was really rather vicious, and I felt I had to step up to defend our old friend k-means. So I started writing up something that shows that those oft-highlighted weaknesses of k-means aren’t nearly bad as people think, and in most cases don’t outweigh the awesomeness that k-means brings to the party.

It started to get quite lengthy, so I’m breaking it up into pieces and maybe I’ll put it all together into one thing later. This post is the first of those pieces.

## Convex sets

“K-means can’t handle non-convex sets”.

*Convex sets: In Euclidean space, an object is convex if for every pair of points within the object, every point on the straight line segment that joins them is also within the object. [Source: Wikipedia.]*

The k-means algorithm, in its basic form, is like making little circular paper cutouts and using them to cover the data points. We can change the quantity and size and position of our paper cut-outs, but they are still round and, thus, these non-convex shapes evade us.

That is, what are doing when we use k-means is constructing a mixture of k-gaussians. This works well if the data can be described by spatially separated hyper-spheres.

Here’s a clustering example, borrowed directly from the sklearn documentation on clustering. These are two slightly entangled banana spheres. That’s *two *non-convex shapes, and they are not spatially separated.

When we try to use k-means on this example, it doesn’t do very well. There’s just no way to form these two clusters with two little circular paper cut-outs. Or three.

## K-means pairs well

But by combining k-means with another algorithm, hierarchical clustering, we can solve this problem. Pairing k-means with other techniques turns out to be a very effective way to draw from its benefits while overcoming its deficiencies. It’s like our theme. I’ll do it again in another post, just you watch.

First, we cluster the data into a large number of clusters using k-means. Below, I’ve plotted the centroids of clusters after k-means clustering using 21. [Why 21? Well, actually, it doesn’t matter very much in the end.]

Then, we take these many clusters from k-means and then start clustering *them* together into bigger clusters using a single-link agglomerative method. That is, we repeatedly pick the two clusters that are closest together and merge them. It is important in this scenario that we use the “single-link” method, in which the distance between two clusters is defined by the distance between the two closest data points we can find, one from each cluster.

Here’s what that looks like:

Woah woah. Did you see that one near the end? The one where we’ve taken 616 data points, formed a whole bunch [I used k=51 for the animation to get lots of colorful frames] of clusters with k-means , and then agglomerated them into … this:

Yup, that one. So pretty.

## So many benefits

You get it already, I’m sure. We’re making lots of those little circles, covering all the data points with them. Then, we are attaching the little circles to each other, in pairs, by repeatedly picking the two that are closest.

K-means and single-link clustering. Combining the two algorithms is a pretty robust technique. It is less sensitive to initialization than pure k-means. It is also less sensitive to choice of parameters. When we have many points, we use an algorithm that is fast and parallelizable. After the heavy lifting is done, we can afford to use the more expensive hierarchical method, and reap its benefits, too.

There are many additional problems with k-means: sensitivity to initialization, the need to pick k, poor performance in high-dimensions. Today we looked at those damn non-convex sets. I’ll dive into some of the others in future posts.

By the way, in the banana shapes solution today, note that we don’t have to specify ahead of time the expected final number of clusters. We specified some arbitrary large number for k, but we finished up with hierarchical clustering. We could use one of many well-studied techniques to decide when to stop clustering. For example, we could automate a stopping rule using concepts of separation and cohesion — see this post for a hint.

Related posts:

# Interpretation of Silhouette Plots (Clustering)

I am cross-posting here one of my answers on Stack Exchange – Cross Validation (that’s like Stack Overflow for statistics). The question had already been answered and accepted when I posted my answer several months later, but I chose to spend some time putting in my thoughts anyway.

I’m particularly interested in the interpretation of simple plots in the context of exploratory data analysis, and am planning to compile resources for data explorers on this subject. My plan is to do this via a wiki — which I have already installed but not yet populated with much information. So busy these days!

**Q: How to interpret the meaning of a Silhouette plot?
**

How does one determine the number of clusters through interpretation of a Silhouette plot?

[I have paraphrased the OP’s question. -AA]

**My answer**

(Click through to my answer on Cross Validated for the most recent version of the answer, which may have changed.)

Sergey’s answer contains the critical point, which is that the silhouette coefficient quantifies the quality of clustering achieved — so you should select the number of clusters that maximizes the silhouette coefficient.

The long answer is that the best way to evaluate the results of your clustering efforts is to start by actually examining — human inspection — the clusters formed and making a determination based on an understanding of what the data represents, what a cluster represents, and what the clustering is intended to achieve.

There are numerous quantitative methods of evaluating clustering results which should be used as tools, with full understanding of the limitations. They tend to be fairly intuitive in nature, and thus have a natural appeal (like clustering problems in general).

Examples: cluster mass / radius / density, cohesion or separation between clusters, etc. These concepts are often combined, for example, the ratio of separation to cohesion should be large if clustering was successful.

The way clustering is measured is informed by the type of clustering algorithms used. For example, measuring quality of a *complete* clustering algorithm (in which all points are put into clusters) can be very different from measuring quality of a threshold-based fuzzy clustering algorithm (in which some point might be left un-clustered as ‘noise’).

The silhouette coefficient is one such measure. It works as follows:

For each point p, first find the average distance between p and all other points in the same cluster (this is a measure of cohesion, call it *A*). Then find the average distance between p and all points in the nearest cluster (this is a measure of separation from the closest other cluster, call it *B*). The silhouette coefficient for p is the difference between *B* and *A* divided by the greater of the two (*max(A,B)*).

We evaluate the cluster coefficient of each point and from this we can obtain the ‘overall’ average cluster coefficient.

Intuitively, we are trying to measure the space between clusters. If cluster cohesion is good (*A* is small) and cluster separation is good (*B* is large), the numerator will be large, etc.

I’ve constructed an example here to demonstrate this graphically.

In these plots the same data is plotted five times; the colors indicate the clusters created by k-means clustering, with k = 1,2,3,4,5. That is, I’ve forced a clustering algorithm to divide the data into 2 clusters, then 3, and so on, and colored the graph accordingly.

The silhouette plot shows the that the silhouette coefficient was highest when k = 3, suggesting that’s the optimal number of clusters. In this example we are lucky to be able to visualize the data and we might agree that indeed, three clusters best captures the segmentation of this data set.

If we were unable to visualize the data, perhaps because of higher dimensionality, a silhouette plot would still give us a suggestion. However, I hope my somewhat long-winded answer here also makes the point that this “suggestion” could be very insufficient or just plain wrong in certain scenarios.

# BARAC: Rank Aware Interval Based Clustering

I read this paper on 10/19/2012.

Authors

Julia Stoyanovich, UPenn

Sihem Amer-Yahia, Yahoo! Research

Tova Milo, Tel Aviv Univ.

Links

Original paper: http://www.icdt.tu-dortmund.de/proceedings/edbticdt2011proc/WebProceedings/papers/edbt/a39-stoyanovich.pdf [PDF]

**[Updated 10/19/2012. I just found a nice slideshare from Julia Stoyanovich.]**

Slideshare: http://www.slideshare.net/yandex/julia-stoyanovich-making-intervalbased-clustering-rankaware-12174065

### Bottom-up Algorithm for Rank Aware (Interval Based) clustering.

Comments

This paper argues that in some scenarios providing search results in a ranked manner is improved by grouping similar results such that the user is more easily able to access a varied set of matching responses. The proposal is to cluster results before presenting them to the user. Clustering is performed *locally*, that is, after applying the filter and by taking into account the user’s selected ranking criterion.

The example used for the paper is based on Yahoo! personal searches. For a particular filter (age range, sex, etc), a user may select a ranking variable (e.g. income, highest to lowest). Without the proposed cluster, the user may see a long list of very similar results (perhaps software engineers) before seeing a different type of result (a different profession), and if uninterested in the first category, would have to wade inconveniently through a long list.

The paper describes ways to measure qualities of clusters, including formal definitions, which are then used as a basis for an algorithm “BARAC” for “Bottom-up Algorithm for Rank Aware (Interval Based) clustering”. It also discusses complexity and computational costs, and results from user tests.

- Interval-based clustering refers to grouping an attribute by a range of values (age between 20 and 25).
- CLIQUE — a rank unaware clustering framework, used both as a point of comparison and a starting template for BARAC. [Agrawal 1998]
- Clustering measures are based on locality, quality, tightness, maximality.

In Conclusion

The paper is clear and easily consumed (I read it on BART), and is sufficiently descriptive to be actionable (even includes pseudo code for BARAC and most sub routines). Discusses some practical issues in implementation. Some general valuable lessons can be extracted.