I have an assignment question:
Suppose you are given a task of finding all the association rules in a database whose
supports are between 20% and 80%, and the accuracy of the rules should be above 70%. Change the algorithm scheme of Apriori to find all the rules satisfying the above requirement.
I know that to get rules with minimum support of 20%, I remove itemsets with less than 20% support from candidate sets. But I am confused about how to apply a maximum threshold on support.
I can't remove the itemsets with support greater than 80% as it will remove itemsets with support within the allowed range down the steps.
FP-growth algorithms are used for Itemset Mining. Is there a way to use these algorithms for Sequential Pattern Mining instead of Itemset Mining?
The FPGrowth algorithm is defined to be used on transactions to find itemsets. Thus, it does not care about the order of items, and each item can only appear once in a transaction.
If you want to apply it to sequences to find sequential patterns, then this is a more general problem. In other words, itemset mining is a special case of sequential pattern mining. To handle this problem, you would need to generalize FPGrowth. First, you would need to modify the FPTree to store sequences where items can appear more than once. This means to change how the branch of the trees are created. But also you would need to change how links between node representing items are treated since the same item can appear multiple times per sequence.
But is it really a good idea? I am not sure about it. There are many sequential pattern mining algorithms. For example, you can use several imlementation in my SPMF data mining library (http://www.philippe-fournier-viger.com/spmf/ ) impltemented in Java, so you don't need to implement it by yourself.
Please suggest me for any kind material about appropriate minimum support and confidence for itemset!
::i use apriori algorithm to search frequent itemset. i still don't know appropriate support and confidence for itemset. i wish to know what kinds of considerations to decide how big is the support.
The answer is that the appropriate values depends on the data.
For some datasets, the best value may be 0.5. But for some other datasets it may be 0.05. It depends on the data.
But if you set minsup =0 and minconf = 0, some algorithms will run out of memory before terminating, or you may run out of disk space because there is too many patterns.
From my experience, the best way to choose minsup and minconf is to start with a high value and then to lower them down gradually until you find enough patterns.
Alternatively, if you don't want to have to set minsup, you can use a top-k algorithms where instead of specifying minsup, you specify for example that you want the k most frequent rules. For example, k = 1000 rules.
If you are interested by top-k association rule mining, you can check my Java code here:
http://www.philippe-fournier-viger.com/spmf/
The algorithm is called TopKRules and the article describing it will be published next month.
Besides that, you need to know that there is many other interestingness measures beside the support and confidence: lift, all-confidence, ... To know more about this, you can read this article: "On selecting interestingness measures for association rules" and "A Survey of Interestingness Measures for Association Rules" Basically, all measures have some problems in some cases... no measure is perfect.
Hope this helps!
In any association rule mining algorithm, including Apriori, it is up to the user to decide what support and confidence values they want to provide. Depending on your dataset and your objectives you decide the minSup and minConf.
Obviously, if you set these values lower, then your algorithm will take longer to execute and you will get a lot of results.
The minimum support and minimum confidence parameters are a user preference. If you want a larger quantity of results (with lower statistical confidence), choose the parameters appropriately. In theory you can set them to 0. The algorithm will run, but it will take a long time, and the result will not be particularly useful, as it contains just about anything.
So choose them so that the result suit your needs. Mathematically, any value is "correct".
I want to develop an Intrusion Detection System (IDS) that might be used with one of the KDD datasets. In the present case, my dataset has 42 attributes and more than 4,000,000 rows of data.
I am trying to build my IDS using fuzzy association rules, hence my question: What is actually considered as the best tool for fuzzy logic in this context?
Fuzzy association rule algorithms are often extensions of normal association rule algorithms like Apriori and FP-growth in order to model uncertainty using probability ranges. I thus assume that your data consists of quite uncertain measurements and therefore you want to group the measurements in more general ranges like e.g. 'low'/'medium'/'high'. From there on you can use any normal association rule algorithm to find the rules for your IDS (I'd suggest FP-growth as it has lower complexity than Apriori for large data sets).
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I have a few sets of questions regarding outlier detection:
Can we find outliers using k-means and is this a good approach?
Is there any clustering algorithm which does not accept any input from the user?
Can we use support vector machine or any other supervised learning algorithm for outlier detection?
What are the pros and cons of each approach?
I will limit myself to what I think is essential to give some clues about all of your questions, because this is the topic of a lot of textbooks and they might probably be better addressed in separate questions.
I wouldn't use k-means for spotting outliers in a multivariate dataset, for the simple reason that the k-means algorithm is not built for that purpose: You will always end up with a solution that minimizes the total within-cluster sum of squares (and hence maximizes the between-cluster SS because the total variance is fixed), and the outlier(s) will not necessarily define their own cluster. Consider the following example in R:
set.seed(123)
sim.xy <- function(n, mean, sd) cbind(rnorm(n, mean[1], sd[1]),
rnorm(n, mean[2],sd[2]))
# generate three clouds of points, well separated in the 2D plane
xy <- rbind(sim.xy(100, c(0,0), c(.2,.2)),
sim.xy(100, c(2.5,0), c(.4,.2)),
sim.xy(100, c(1.25,.5), c(.3,.2)))
xy[1,] <- c(0,2) # convert 1st obs. to an outlying value
km3 <- kmeans(xy, 3) # ask for three clusters
km4 <- kmeans(xy, 4) # ask for four clusters
As can be seen in the next figure, the outlying value is never recovered as such: It will always belong to one of the other clusters.
One possibility, however, would be to use a two-stage approach where one's removing extremal points (here defined as vector far away from their cluster centroids) in an iterative manner, as described in the following paper: Improving K-Means by Outlier Removal (Hautamäki, et al.).
This bears some resemblance with what is done in genetic studies to detect and remove individuals which exhibit genotyping error, or individuals that are siblings/twins (or when we want to identify population substructure), while we only want to keep unrelated individuals; in this case, we use multidimensional scaling (which is equivalent to PCA, up to a constant for the first two axes) and remove observations above or below 6 SD on any one of say the top 10 or 20 axes (see for example, Population Structure and Eigenanalysis, Patterson et al., PLoS Genetics 2006 2(12)).
A common alternative is to use ordered robust mahalanobis distances that can be plotted (in a QQ plot) against the expected quantiles of a Chi-squared distribution, as discussed in the following paper:
R.G. Garrett (1989). The chi-square plot: a tools for multivariate outlier recognition. Journal of Geochemical Exploration 32(1/3): 319-341.
(It is available in the mvoutlier R package.)
It depends on what you call user input. I interpret your question as whether some algorithm can process automatically a distance matrix or raw data and stop on an optimal number of clusters. If this is the case, and for any distance-based partitioning algorithm, then you can use any of the available validity indices for cluster analysis; a good overview is given in
Handl, J., Knowles, J., and Kell, D.B.
(2005). Computational cluster validation in post-genomic data analysis.
Bioinformatics 21(15): 3201-3212.
that I discussed on Cross Validated. You can for instance run several instances of the algorithm on different random samples (using bootstrap) of the data, for a range of cluster numbers (say, k=1 to 20) and select k according to the optimized criteria taht was considered (average silhouette width, cophenetic correlation, etc.); it can be fully automated, no need for user input.
There exist other forms of clustering, based on density (clusters are seen as regions where objects are unusually common) or distribution (clusters are sets of objects that follow a given probability distribution). Model-based clustering, as it is implemented in Mclust, for example, allows to identify clusters in a multivariate dataset by spanning a range of shape for the variance-covariance matrix for a varying number of clusters and to choose the best model according to the BIC criterion.
This is a hot topic in classification, and some studies focused on SVM to detect outliers especially when they are misclassified. A simple Google query will return a lot of hits, e.g. Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction by Thongkam et al. (Lecture Notes in Computer Science 2008 4977/2008 99-109; this article includes comparison to ensemble methods). The very basic idea is to use a one-class SVM to capture the main structure of the data by fitting a multivariate (e.g., gaussian) distribution to it; objects that on or just outside the boundary might be regarded as potential outliers. (In a certain sense, density-based clustering would perform equally well as defining what an outlier really is is more straightforward given an expected distribution.)
Other approaches for unsupervised, semi-supervised, or supervised learning are readily found on Google, e.g.
Hodge, V.J. and Austin, J. A Survey of Outlier Detection Methodologies.
Vinueza, A. and Grudic, G.Z. Unsupervised Outlier Detection and Semi-Supervised Learning.
Escalante, H.J. A Comparison of Outlier Detection Algorithms for Machine Learning.
A related topic is anomaly detection, about which you will find a lot of papers.
That really deserves a new (and probably more focused) question :-)
1) Can we find outliers using k-means, is it a good approach?
Cluster-based approaches are optimal to find clusters, and can be used to detect outliers as
by-products. In the clustering processes, outliers can affect the locations of the cluster centers, even aggregating as a micro-cluster. These characteristics make the cluster-based approaches infeasible to complicated databases.
2) Is there any clustering algorithm which does not accept any input from the user?
Maybe you can achieve some valuable knowledge on this topic:
Dirichlet Process Clustering
Dirichlet-based clustering algorithm can adaptively determine the number of clusters according to the distribution of observation data.
3) Can we use support vector machine or any other supervised learning algorithm for outlier detection?
Any Supervised learning algorithm needs enough labeled training data to construct classifiers. However, a balanced training dataset is not always available for real world problem, such as intrusion detection, medical diagnostics. According to the definition of Hawkins Outlier("Identification of Outliers". Chapman and Hall, London, 1980), the number of normal data is much larger than that of outliers. Most supervised learning algorithms can't achieve an efficient classifier on the above unbalanced dataset.
4) What is the pros and cons of each approach?
Over the past several decades, the research on outlier detection varies from the global computation to the local analysis, and the descriptions of outliers vary from the binary interpretations to probabilistic representations. According to hypotheses of outlier detection models, outlier detection algorithms can be divided into four kinds: Statistic-based algorithms, Cluster-based algorithms, Nearest Neighborhood based algorithms, and Classifier-based algorithms. There are several valuable surveys on outlier detection:
Hodge, V. and Austin, J. "A survey of outlier detection methodologies", Journal of Artificial Intelligence Review, 2004.
Chandola, V. and Banerjee, A. and Kumar, V. "Outlier detection: A survey", ACM Computing Surveys, 2007.
k-means is rather sensitive to noise in the data set. It works best when you remove the outliers beforehand.
No. Any cluster analysis algorithm that claims to be parameter-free usually is heavily restricted, and often has hidden parameters - a common parameter is the distance function, for example. Any flexible cluster analysis algorithm will at least accept a custom distance function.
one-class classifiers are a popular machine-learning approach to outlier detection. However, supervised approaches aren't always appropriate for detecting _previously_unseen_ objects. Plus, they can overfit when the data already contains outliers.
Every approach has its pros and cons, that is why they exist. In a real setting, you will have to try most of them to see what works for your data and setting. It's why outlier detection is called knowledge discovery - you have to explore if you want to discover something new ...
You may want to have a look at the ELKI data mining framework. It is supposedly the largest collection of outlier detection data mining algorithms. It's open source software, implemented in Java, and includes some 20+ outlier detection algorithms. See the list of available algorithms.
Note that most of these algorithms are not based on clustering. Many clustering algorithms (in particular k-means) will try to cluster instances "no matter what". Only few clustering algorithms (e.g. DBSCAN) actually consider the case that maybe not all instance belong into clusters! So for some algorithms, outliers will actually prevent a good clustering!