Tagged: clustering

Discovering and visualizing songs with similar trends on the British Top 40 Charts from 1990 to 2014

Share Button

I often wondered what is an average lifetime of a pop song on the charts. If one follows music, it becomes intuitively apparent that there are in fact several types of hits. Some stay on the charts for many weeks, and others barely make it, then immediately slip out.

So I set about discovering groups of songs with similar trends, as they moved on weekly British Top 40 Chart from 1990 to 2014. A total of 1284 different songs appeared on the charts in that period. After a series of experiments, 100 groups were arbitrarily decided on. Position data for each song was collected across the weeks, then the songs were grouped using k-means clustering.

The result is part interactive, part static visualization, consisting of an exploratory chart and 100 small charts showing each separate group.

Check it out here! Or click the image below.Song trends over time in a typical group

Song trends over time in a typical group

 

To group the songs, the data was first scraped from www.officialcharts.com, then arranged in format suitable for k-means clustering. The visualization was constructed with d3.

And here are some of the small multiples.

Some of the 100 different groups. Click image for more.
Some of the 100 different groups. Click image for more.

Grouping countries according to flag similarity

Share Button

This topic is apparently interesting enough that it warrants its own discussion on Quora. People there are relying on keen observational powers of human mind, but for this article, I tried to group the flags algorithmically.

I plotted the results on the map below. Countries with same colors have similar flags. The brighter the color, the bigger the group of countries with similar flags.

Launch the interactive viewer to explore the matter interactively.

Countries by flag similarity
Countries by flag similarity

 

Here are some flag groups. To see them all, click the image above.

flags_7flags_80 flags_124 flags_27

How I grouped the flags

I used a machine learning algorithm called k-means clustering. It’s really a rudimentary exercise, but the results are good enough to publish on this wee blog.

The algorithm accepts units to be grouped as vectors, so I had to vectorize the images first, that is to say, convert them in a long string of numbers. Each image was partitioned into a grid, then the average color  value for each cell was computed. The grid was 24 x 24 cells big. I found that enough for simple flags.  These color values were converted into HSB color space and experimentally weighted, then copied into a vector. These vectors were fed into the k-means algorithm with requested number of individual clusters set to 120 (there are 240 different flags). You can see results in the viewer.

Number of clusters was set experimentally, and the clustering is not perfect. For example, Canadian is grouped with some very unlikely lookalikes.

See also the other post with k-means clustering, K-means clustering with Processing.js

 

Enhanced by Zemanta

Data-driven drinking: ingredients and brands in the cocktail shaker

Share Button

A better title for post would probably be “What kinds booze to drink together to get drunk in style, according to those who write, compile, publish, test and enjoy cocktail recipes”. Continuing from previous posts, I wanted to see how does it look a network of ingredients of all possible cocktail recipes, and if it’s possible to divide them into sensible groups, so that they would be instantly recognizable and even helpful to experienced and casual drinkers alike.

To do this, more than 25,000 recipes from Drinksmixer.com and Drinksnation.com were scraped, a network was constructed with Gephi, and visualized here below. Dot size reflects the count of that particular ingredient in all analyzed recipes. Dots of same color frequently appear together in recipes. One could say that one can hardly make a mistake if one combines three ingredients of the same color and drinks the concoction.

The map below is interactive, try panning and zooming with mouse or use the control in the upper left-hand corner.

I see five major groups of ingredients, but your alcohol proof may vary. Actually I suspected something like that:

  • ice is in its own group. For some reason it also contains tequila,
  • milky drinks are in their own group (gray-blue),
  • salty and spicy drinks are also in an easily recognizable group (pink),
  • blue group is dominated by vodka and rum,
  • green group mostly has gin and tangy juices, and
  • red group mostly contains fruit schnappses and liqueurs.

You can download hi-res static images here: black background | white background.

For a more mobile-friendly, searchable map with advanced interactivity, click here (Sigma.js). Clicking on an ingredient on this map will show a list of all connected ingredients. Clicking on an element in the list will show a subgraph.

Most recipes contained preferred brands for spirits and fruit juices, so I constructed another diagram. It shows which brands are usually grouped together in drinks.

Here is the interactive map:

Download hi-res static images here: black background | white background.
For a searchable map with advanced interactivity, click here. Clicking on a brand on this map will show a list of all connected brands.

I find it funny that Everclear, Kool-Aid and Mountain Dew are so close. Does that mean that people just pour 100% ethanol and caffeinated water in a jug and drink that? Possibly.

 

Coming up next: data-driven cooking.

Visualizing drug talk on bluelight.ru

Share Button

In mainstream media, there’s not a lot to be found about recreational drugs except horror stories and arguments for prohibition. From time to time we also hear that Steve Jobs liked to drop acid when he was young, that countless Vietnam vets easily kicked heroin habit upon coming home, and, as US-fed-sponsored study found out, that psychedelic mushrooms can bring a lasting and positive personality change in more than half of those who take them.

Where to find good information? There exist internet communities, so-called harm-reduction forums, where one can spend a few hours to discover that the truth is not black and white. Surely junkies exist, and using meth daily is not a life strategy anyone could recommend, but not all drugs were created equal. There are many classes of recreational drugs, each acting on specific chemical pathways in body – uppers on dopamine, hallucinogens on serotonin, downers on GABA, etc.

Mapping drugs

I thought it would be nice to visualize these drug groups based on what users of harm-reduction forums say, so I analyzed around 1.2 million posts on bluelight.ru and constructed a simple diagram that tells a lot. It was constructed in such a way that drugs that are frequently mentioned together, appear together. Circle radii are proportional with frequency of appearance of the same drugs in the posts. Methodology is explained at the bottom of the post.

Here’s the diagram, pan and zoom at will:

Click here to peruse a clickable, searchable version of the same diagram (give it a second to load). To download a high-resolution image (8000 x 6000), click here (black) or here (white).

The drug groups are color coded for better readability. Starting from the top:

  • light blue group: mostly antidepressives – SSRIs such as Prozac (fluoxetine), Zoloft and such.
  • violet group:  mainly contains benzodiazepines such as Xanax, Valium, and Lorazepam, which are commonly abused, but there are a lot of other downers there.
  • orange group: opiates and opioids, soch as heroin, oxycontin and the like. There were so many mentions of “opiates” without referring to a specific chemical that I considered it would be a pity to leave the word out.
  • dark yellow group on the right: mostly dissociatives such as ketamine and DXM, but there’s also a subgroup on the right side. It forms a larger group, mixed with differently colored drugs, that could be called “shamanic corner”, as it mostly contains so-called entheogens and natural concoctions such as ayahuasca.
  • light orange group: mainly nootropics such as Piracetam. Some use them to enhance a psychedelic or MDMA experience, but they have a more general use as memory, intelligence and sensory enhancers.
  • red group: I don’t know what to call this, but these are “working man’s drugs”. The common drugs that we hear about in the media. Some of these drugs are not considered drugs at all, for example alcohol and tobacco, but the Bluelight discussions show that they are very common. Thinking about it, one must have something to drink while one insufflates synthetic powders, and a cigarette is also a good thing to have while waiting for something stronger to take hold.
  • green group: psychedelic drugs such as shrooms, LSD, DMT and mescaline, along with many newer variations and analogs, such as 2C-X family, the DMT analogs and the whole Tihkal inventory.
  • blue group: Ecstasy (MDMA) and newer stimulants and entactogens, such as methylone, mephedrone, etc. “Plant foods” and “bath salts” are in this category.

Mapping effects

Simply mapping out the drugs is nice, but additional step seemed in order: mapping coincidence of various effects the drugs have on users. Again, posts were analyzed, but in addition to drugs, some (not all!) common effects were extracted and mapped in a network. Result is in the diagram below. Darker dots are effects, lighter are drugs. Size is again proportional to number of mentions in all posts.

Click here to peruse a clickable, searchable version of the same diagram. To download a high-resolution image, click here (black) or here (white).

Note that above diagram does not indicate semantic relationships between drugs and their effects. For example, why is “marijuana” close to “death”? Maybe there was a lot of talk about fear of death that the marijuana experience helps to resolve, or maybe people like to describe how they are dying of laughter while smoking weed. I honestly don’t know. I suspect it’s because of close relationship between mentions (not necessarily use!) of marijuana and those of alcohol, cocaine and methamphetamine, which could have a more significant relation with death or dying.
What’s really notable is heavy clustering of adverse effects around opiates, and relative absence of same around psychedelics. Based on Bluelight data, I can safely conclude that psychedelic drugs do not cause users to complain a lot, except maybe mentioning hallucinations and visuals, but, well …

Drug use over the years

My whole database contains posts from 2010 until March 2013. Here’s an analytical tool to better understand what’s going on in the recreational drug market community. Time is on horizontal axis, while the proportion of posts mentioning specific drug relative to all posts in that month is on the vertical axis.

Play around with interactive chart to discover emerging trends, or simply to behold the wax and wane of specific chemicals as they compete for users’ neurological apparatuses, while their manufacturers are temporarily evading ever stricter analog laws:

Commentary: Bluelight is a harm reduction forum, historically established for the users to be able to tell a good Ecstasy pill from the bad, so MDMA is the most mentioned drug. Use of “classic” drugs doesn’t change much, but it’s interesting to note the rise of new “research chemicals” such as NBOME family, new cathinones (3-MMC), new synthetic canabinoids (STS-135) and different amphetamines, prevalently methamphetamine. You can also see how the newly banned drugs, for example mephedrone, go out of use, and their analogs, in this case 3-MMC, replace them.

Methodology and tools

First, all the Bluelight forums were crawled and contents, dates and other metadata of all posts put into a SOLR index. That took approximately two days of not too aggressive load on their server (thanks Bluelight for not banning my IP).
To make first two network diagrams, undirected graphs were constructed with JGraphT library so that all extracted entities – drugs and effects – in every post were connected as nodes. Mentions of all extracted entities were counted to make the dots size show frequencies, not network degrees. That yielded complete graphs to be visualized with Gephi. Gephi files were exported to a TileMill-friendly format to render map tiles. Tiles are displayed on the site using Leaflet.
To make the interactive chart, SOLR was used to produce time series. Data was then packed into suitable format for the Flot library to be able to display.
To extract entities, two dictionaries were used – one for drugs, one for effects. You can download them here: drugs / effects.
If anyone is interested in the SOLR core, I can put it on Dropbox. Send me a note, my email is on the About page.

What is not here, but could be

  • analysis of effects that specific drugs have over time
  • a chart of effects only
  • some different visualization that could help to establish relationships between specific drugs and effects they have. For example, it’s been known for some time that mephedrone and various dragonflies have vasoconstrictive effects. Maybe some other relationship could be inferred that way.
  • first map should be clickable to search on Wikipedia, I’ll add that as soon as I figure out the Wax lib.

I may revisit this theme in the future.

Some pics:
drug_effects_diagram

Drug talk visualizations

K-means clustering with Processing.js

Share Button

K-means clustering is an algorithm to quickly group a large quantity of data. It’s used in variety of ways, from statistical analysis to improving usability of user interfaces. If you read Google News, you’re probably familiar with the way they group similar news items together. When I first saw that, I thought there must be some serious language processing and semantics behind that – that they somehow extract meaning from the articles and group them together accordingly.

Turns out that it’s a little easier than that. Article abstracts are split into words, and for each article, a multidimensional vector consisting of these words is constructed.  These vectors are then put into n-dimensional space, in which number of dimensions corresponds to the total number of different words in all articles analyzed. Then a clustering algorithm is run on the articles in this space. It yields groups of correlated articles based on their word vectors.

One clustering algorithm to do that is k-means. It works like that (I cite Manning’s excellent book “Algorithms of Intelligent Web” under fair use):

The k-means algorithm randomly picks k points that represent the initial centroids of the candidate clusters. Subsequently the distances between these centroids and each point of the set are calculated, and each point is assigned to the cluster with the minimum distance between the cluster centroid and the point. As a result of these assignments, the locations of the centroids for each cluster have now changed, so we reevaluate the new centroids until their locations stop changing. This particular algorithm for k-means is attributed to E.W. Forgy and to S.P. Lloyd, and has the following advantages:

  • It works well with many metrics.
  • It’s easy to derive versions of the algorithm that are executed in parallel—when
  • the data are divided into, say, N sets and each separate data set is clustered, in
  • parallel, on N different computational units.
  • It’s insensitive with respect to data ordering.

At this point you may wonder, what happens if the algorithm doesn’t stop? Don’t worry! It’s guaranteed that the iterations will stop in a finite number of steps. In practice, the algorithm converges quickly (that’s the mathematical jargon).

Of course you need to use Apache Mahout, preferably in combination with Solr, to do that on text in production environment. But it’s also possible to experiment in Processing, albeit for this experiment I w0n’t use text, but points in space. You can see this in the Processing.js applet below. It fills 3D space with random points, adds cluster nodes and then optimizes their positions so that every point belongs to a cluster, coloring the points belonging to each cluster in same color. Resulting configuration is in fact a Voronoi diagram in 3D, which is related to Delaunay triangulation.

Click anywhere on screen to restart, controls to refresh/stop, or select numbers of clusters and points. I suggest downloading the original sketch here, it runs much faster.

Clusters:
    Points:
      
   
width=”800″ height=”600″>Your browser does not support the canvas tag.