I have a set of university courses (around 30000). Each course have the following attributes, here is an example:
Title: Machine Learning
Institution name: Department of Information Technology
Description: After the course the student should be able to: set up and solve typical machine learning problems, by implementation or
with simulation tools, determine different learning methods
applicability for different types of learning problems, ie know the
methods strengths and weaknesses, set a good representation of the
data, recognize the typical effects of bad choices and determine how
to improve the results and describe how and why the machine learning
and natural computation methods.\
I want to cluster these courses to help users with suggestions on similar courses that they liked or searched for. An example would be if a user is searching for "Machine learning" the system could suggest "Artificial intelligence" since the content of those courses are a little bit similar. I am thinking of three different approaches to do this.
TF-IDF + spherical K-means
levenstein similarity + hierarchical agglomerative clustering
Jaccard similarity + minhash + locality sensitive hashing (LSH)
My plan is to try this methods in Matlab, come up with the best one and eventually (if I have time enough) implement it. I am planning to do this as my master thesis, all information on this would be highly appreciated. Does it make sense to compare this three methods on the given dataset?
Thank you.
Related
Take the following link as an example: http://www.yelp.com/biz/chef-yu-new-york.
In the section called 'Review Highlights', there are 3 phrases (spicy diced chicken, happy hour, lunch specials) that are highlighted based on reviews submitted by users. Obviously, these are the phrases that appeared most often, or longest phrases that appeared often, or some other logic.
Their official explanation is this:
In their reviews, Yelpers mentioned the linked phrases below a lot.
And these aren't any old common phrases, they're also the ones that
our Yelp Robots have determined are unique and good, quick ways to
describe this business. Click any of the phrases to see all the
reviews that mention it.
My question is, what did they use to mine the text input to get these data points? Is it some algorithm based on Lempel Ziv, or some kind of map reduce? I was not a CS major, so probably am missing something foundational here. Would love some help, theories, etc.
Thanks!
I don't have any insight on the exact algorithm Yelp is using but this is a common problem in natural language processing. Essentially you want to extract the most relevant collocations (http://en.wikipedia.org/wiki/Collocation).
A simple way to do this is to extract a list of n-grams with the highest PMI (pointwise mutual information). This SO question explains how to do this using Python and the nltk library:
How to extract common / significant phrases from a series of text entries
Lempel-Ziv is a data compression algorithm, and map-reduce is a technique for data processing. The former is probably not involved, and the latter is generally useful but not relevant here.
Without knowing the details of Yelp's code, it's impossible to say for sure, but it seems likely that their "review highlights" are simply based on tabulating all phrases that appear in reviews for this business, then displaying ones which are more common in reviews for this business than for other businesses. Some amount of natural language processing is likely to be involved to ensure that it picks noun phrases.
I have started learning Data Mining and wish to create a small project in C++/Java that allows me to utilize a database, say from twitter and then publish a particular set of results (for eg. all the news items on a feed). I want to know how to go about it? Where should I start?
This is a really broad question, so it's hard to answer. Here are some things to consider:
Where are you going to get the data? You mention twitter, but you'll still need to collect the data in some way. There are probably libraries out there for listening to twitter streams, or you could probably buy the data if someone is selling it.
Where are you going to store the data? Depending on how much you'll have and what you plan to do with it, a traditional relational database may or may not be the best fit. You may be better off with something that supports running mapreduce jobs out-of-the box.
Based on the answers to those questions, the choice of programming languages and libraries will be easier to make.
If you're really set on Java, then I think a Hadoop cluster is probably what you want to start out with. It supports writing mapreduce jobs in Java, and works as an effective platform for other systems such as HBase, a column-oriented datastore.
If your data are going to be fairly regular (that is, not much variation in structure from one record to the next), maybe Hive would be a better fit. With Hive, you can write SQL-like queries, given only data files as input. I've never used Mahout, but I understand that its machine learning capabilities are suited for data mining tasks.
These are just some ideas that come to mind. There are lots of options out there and choosing between them has as much to do with the particular problem you're trying to solve and your own personal tastes as anything else.
If you just want to start learning about Data Mining there are two books that I particularly really enjoy:
Pattern Recognition and Machine Learning. Christopher M. Bishop. Springer.
And this one, which is for free:
http://infolab.stanford.edu/~ullman/mmds.html
Good references for you are
AI course taught by people who actually know the subject,Weka website, Machine Learning datasets, Even more datasets, Framework for supporting the mining of larger datasets.
The first link is a good introduction on AI taught by Peter Norvig and Sebastian Thrun, Google's Research Director, and Stanley's creator (the autonomous car), respectively.
The second link you get you to Weka website. Download the software - which is pretty intuitive - and get the book. Make sure you understand all the concepts: what's data mining, what's machine learning, what are the most common tasks, and what are the rationales behind them. Play a lot with the examples - the software package bundles some datasets - until you understand what generated the results.
Next, go to real datasets and play with them. When tackling massive datasets, you may face several performance issues with Weka - which is more of a learning tool as far as my experience can tell. Thus I recommend you to take a look at the fifth link, which will get you to Apache Mahout website.
It's far from being a simple topic, however, it's quite interesting.
I can tell you how I did it.
1) I got the data using twitter4j.
2) I analyzed the data using JUNG.
You have to define a class representing edges and a class representing vertices.
These classes will contain the attributes of the edges and vertices.
3) Then, there is a simple function to add an edge g.addedge(V1,V2,edgeFromV1ToV2) or to add a vertex g.addVertex(V).
The class that defines edges or vertices is easy to create. As an example :
`public class MyEdge {
int Id;
}`
The same is done for vertices.
Today I would do it with R, but if you don't want to learn a new programming language, just import jung which is a java library.
Data mining is broad fields with many different techniques; classification, clustering, association and pattern mining, outlier detection, etc.
You should first decide what you want to do and then decide wich algorithm you need.
If you are new to data mining, I would recommend to read some books like Introduction to Data Mining by Tan, Steinbach and Kumar.
I would like to suggest you to use python or R for data mining process. Doing work with java or c , it bit difficult in the sense you need to do a lot coding
I'm curious about how works feature on many social sites today.
For example, you enter list of movies you like and system suggests other movies you may like (based on movies that like other people who likes the same movies that you). I think doing it straight-sql way (join list of my movies with movies-users join with user-movies group by movie title and apply count to it ) on large datasets would be just impossible to implement due to "heaviness" of such query.
At the same time we don't need exact solution, approximate would be enough. I wonder is there way to implement something like fuzzy query to traditional RDBMS that would be fast to execute but has some infelicity. Or how such features implemented on real systems.
that's collaborative filtering, or recommendation
unless you need something really complex the slope one predictor is one of the more simple ones it's like 50 lines of python, Bryan O’Sullivan’s Collaborative filtering made easy, the paper by Daniel Lemire et al. introducing "Slope One Predictors for Online Rating-Based Collaborative Filtering"
this one has a way of updating just one user at a time when they change without in some cases for others that need to reprocess the whole database just to update
i used that python code to do predict the word count of words not occurring in documents but i ran into memory issues and such and i think i might write an out of memory version maybe using sqlite
also the matrix used in that one is triangular the sides along the diagonal are mirrored so only one half of the matrix needs to be stored
The term you are looking for is "collaborative filtering"
Read Programming Collective Intelligence, by O'Reilly Press
The simplest methods use Bayesian networks. There are libraries that can take care of most of the math for you.
I have recently become interested in the field(s) of data mining and machine learning. The idea of going through huge datasets and trying to correlate hidden patterns and trends is fascinating. So far I have done the following
Used Weka to load simple data sets and generate decision trees
Continously read books, wiki's, blogs and SO on the same
Started playing around SQL Server DM and Python API's
Have an idea on options of freely available data sets on the web(freedb, UN etc)
What is hindering me is the minute I try to go beyond classification/associsciation and into priori/apriori algorithms I am stuck because understanding mathematical equations and logic is not(to put it modestly) one of my strong points.
So my question would be are there anybody in the Data mining field(in the role of product owner or builder) who are not naturally mathematicians? If so, how would you approach in undestanding the field since free tools like Weka and Rapid-miner both expects some mathematical/statistical background?
P.S: Excuse me if I made some mistake in the query like mixing Data mining and analytics when they are separate as I am still getting my feet wet. I hope my core question is clear.
Well, being able to do some analysis of what the data mining models are showing is absolutely vital. However, these days all of the math and statistics are taken care of by the data mining models. You don't need to understand the math behind them (although it helps).
For example, you can look through the SQL Server Analysis Services Data Mining Algorithms and see that even the technical reference is how to use these implementations, not how to recreate them.
If you can understand the business cases and you can understand what the data mining is telling you, there's really no need to delve into the math behind it.
As for some of the free tools, I've never used them, so I can't speak to them. However, I'm a big fan of SSAS and those data mining models, which don't require an extensive mathematical background.
As Eric says, and as far as you only intend to use the existing algorithms and APIs and make sense from them, I don't see problems with the required math/statistics skill set (anyway, you'll need some previous basic knowledge/level).
Now, if you intend to do research or if you want to improve or modify existing algorithms, or why not, create your own algorithms, then math and statistics is a MUST. I just started doing some research in this area, and I'm still trying to fill my skills gap =)
I can find the technical explanation of what data mining is in a book or on Wikipedia, but I'm wondering what sort of development does it exactly involve? Is it more about using tools or more about writing tools? Is it really any much different from other domains when it comes to R&D?
Data Mining is the process of discovering interesting patterns in large amounts of data. It is not querying data, which is just what user Treb describes (sorry Treb).
To understand DM from a developer's perspective, you should read the book Programming Collective Intelligence by Toby Segaran.
In my experience (I'm a former data miner :-)), it's a mixture of using tools and writing tools. A lot of the time, the tools you need to analyse the particular data set don't exist, so you have to write them yourself first. It can be very interesting but you often need quite a different approach to the sort of programming I do now (embedded wireless), for example.
You really ought to change the accepted answer on this question so it doesn't mislead those who come across it.
Saying that querying a database IS data mining because "[h]ow would you discover any pattern in your data without querying first?" is like saying opening your car door is driving because "how else would you be able to drive somewhere without opening the car door first."
You can read your data out of a text file if you want. My first data mining assignment used data sets from the UCI repository and those are almost all text files.
If you want to learn about data mining start by looking up clustering and classification. Learn about decision trees and rule based classification. Then look at k-nearest-neighbor and k-means. After that if you really want to see what data mining is all about look at Chameleon, DBScan, and Support Vector Machines. Don't necessarily learn the minutiae of the last three (they're pretty complex and math heavy) but understanding the abstract idea of what happens will tell you all you need to know in order to use the many tools and libraries that are available for each strategy.
These are only the algorithms that popped into my head just now. There are so many others that I don't recall or don't even know yet.
Data mining is about searching large quantities of data for hidden patterns. Web 2.0 example: News corp uses its site myspace.com as a large data mine to determine what movies and products to promote. They write software to identify trends in the data that it's users post to the site. News corp does this to gather information useful for advertising campaigns and market predictions. It's different from other domains of R&D in that from a data givers perspective its passive. Rather than going out on the street and asking people in person what movies they are likely to see this summer and other such questions, the data mining tools sort out these things by analyzing data given by users voluntarily.
Wikipedia actually does have a pretty good article on it:
- http://en.wikipedia.org/wiki/Data_mining
Data Mining as I say is finding patterns or trends from given data. A developer perspective might be in applications like Anti Money Laundring... Where given a pattern you will search data for that given pattern. One other use is in Projection Softwares... where you project a result or outcome in future against a heuristic by studying recognizing the current trend from data.
I think it's more about using off the shelf tools rather than developing your own. An academic example of that kind of tools might be WEKA. Of course, you still have to know what algorithms use, how to preprocess data (very important this part), etc.
In R&D I don't have much idea, but it should be like almost everything: maths, statistics, more maths...
On the development level, data mining is just another database application, but with a huge amount of data.
The mining itself is done by running specific queries on the database. It's in the creation of the queries where the important work is done. They of course depend on the data model, and on the hypotheses, what sort of trends the customer expects to find.
Therefore, the fine tuning of the queries usually can't be done in development, but only once the system is live and you have live data. Then the user can test his hypotheses and adapt the queries to show him the trends he is looking for.
So from a dev point of view, data maining is about
Managing large sets of data in your client (one query may return 100.000 rows of data)
Providing the user (who may know nothing about SQL or relational databases in general) with an effective way to modify his queries and view the results.