A priori is algorithm used in affinity analysis. A set of rules is generated, usually implications, that describe dataset. Finding frequent datasets from the transaction database is a popular task among data mining appliances, which isn’t as simple as it initially seems. The reason is computational complexity, which goes extremaly extremely high when it comes to very large databases (I like the fancy way they described it in Top 10 Algorithm in DM: combinatorial explosion).
The idea of A priori is: find frequent itemsets (frequent means one with previously assigned level of support) and then generate rules that comply previously assigned level of confidence. There are candidate itemsets generated and they are the base to find n-element frequent itemsets (1st step in procedure is to find one-element frequent itemsets, and then repeat, eliminating itemsets which support is not sufficient). the procedure of generating candidate and frequent sets is repeated simply for the possible number. The main point exploits monotonicity: ?if an itemset is not frequent, any of its superset is never frequent? (again Top 10 Algo in DM). Smart way to eliminate itemsets.
A priori is one of the most important algorithms in data mining. The other ideas to make it even more efficient are e.g. the new ways to create candidate itemsets – hashing techniques (smaller candidate itemesets), partitioning (divide the problem into smaller ones and explore them separately – if only real-life problems work this way!) or sampling. Important improvement of A priori algorithm is FP-growth algorithm, which supports compression (without losing important information) and then partitioning.
Despite of the fact A priori is rather simple, easy implementation and proper results make it serious solution in many problems.
K Nearest Neighbours is a basic classification algorithm. The idea comes probably from the extension of Rote classifier, which is as simple as point system in ‘Whose line is it anyway’. System memorizes whole training set and classifies only items that have exactly same values as in training set. Obvious disadvantage is there will be a lot of unclassified objects. The “next generation” of the concept says the classification occurs using the value of the nearest point in dataset. Comparing to previous way it is a huge difference, but still – system is vulnerable to noise and outliers.
KNN is (comparing to previous strategies) a bit more sophisticated. Algorithm finds a group of k-objects in training set under the condition of “distance” and according to the findings classifies the new object to the previously given class (cluster), respecting weights set to neighbours. Important issues are:
- number of neighbours (it is important because it is in the name of the algo anyway)
- the meaning of distance
- training set is the basic
Parameters are very imporant to the results and I am going to write another post to discuss a little bit more about.
The procedure goes:
- Get the training set remembered (and prepared to update dynamically if data comes continously)
- Measure the distance between new object and object to training set, to find the nearests
- Use collected information to classify new object
In spite of the fact, building the model using kNN is not very difficult task, costs of classification are relatively high. Comparing new object with whole training set (lazy learning) is responsible for that and it is especially visible in large datasets. There are some techniques that reduce the amout of computation – from simply editing training set (sometimes results are even better than classification with larger database) to proximity graphs.
Sources: [Top 10 algorithms in data mining, Springer 2008]
K-means algorithm (centroid algorithm; LGB) is a simple algorithm to partition given data into clusters. The main purpose is to keep the similarity of the data and simultanously to keep the minimal error. K-means is greedy algo type. The main idea is: choose randomly c clusters and then set all the objects that are close to the randomly chosen cluster to it’s class. Then update the center of the set of objects – relocate the central point, and again check if all objects are clustered to the nearest centroid. If not – update. Repeat acordingly.
The k-means complexity is O(cni). The main disadvantages is outliers and noise can influence the result badly. The solution to the first problem is not to use means but medoids, which are not very sensitive to outliers (we just take the center of the set of data, not mean). There is also a danger to stuck in the local optimum – using k-means algo we always have to start several times using different starting sets of starting points.
However, k-means algorithm is the most popular partition algorithm. It is simple, easy to implement and scalable. Moreover, it is possible to use it with dynamic data. Improvement of k-means algorithm is generally connected with making it suitable for very large datasets – the best tries are kd-trees or triangular inequality.
Looking at both data mining and web mining, there’s one main difference. When data mining operates generally on the content, web mining uses also structure of the data:
- WEB CONTENT MINING operates on the content, most of the time it’s text (maybe that’s why WM is called also text mining)
- WEB LINKAGE MINING is getting information from the structure of sites.
- WEB USAGE MINING is getting information from logs – i.e. tracking user’s movement from one page to another
It gives a certain amout of possibilities that doesn’t count in usual data mining.
Web mining is generally a data mining branch. Introducting Web mining I want to take one step back and present some thoughts about data mining.
Data mining or data exploration is set of techniques used to automatically discover non-trivial relations, patterns and schemes in large data collections. In other words, we are looking for deep-hidden knowlegde in very lagre datasets (in web mining case – the Internet), and we only accept automatic solutions. Why? For better understanding. Having the mechanism, we can ask much more difficult questions (comparing to i.e. sql).
At this point, we can say that web mining is data mining with the Internet as the dataset.
Let’s take a short look at the appliance of web mining:
- data classification (i.e. customers’ sentiment, reviews…)
- natural language processing (NLP, but don’t confuse with neuro-linguistic programming)
- www personalization
- knowledge management
- …
Sources:
wazniak.mimuw.edu.pl – data mining (.pps), wikipedia, Bing Liu – Web Mining 2005 Tutorial.
Hello.
This blog is created to support me in my way to explore aspects of web mining. Firstly, I try to include basic informations about web mining, slowly increasing level of the articles. Meanwhile I’ll write my thesis, based also on web content mining.
Another purpose is to disciple me in getting more experienced in the subject, as it is rather new matter for me. I hope I’d release up to 2 articles – entries per week.
Greetings, author.
This blog contains articles related to web mining, data mining, knowledge management and internet exploration. Other refer to web 2.0, internet boards and projects executed by author.