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- 1. 1 Dept of EEE K-means Algorithm
- 2. 2 Abstract k-Means is a rather simple but well known algorithms for grouping objects, clustering. Again all objects need to be represented as a set of numerical features. In addition the user has to specify the number of groups (referred to as k) he wishes to identify. Each object can be thought of as being represented by some feature vector in an n dimensional space, n being the number of all features used to describe the objects to cluster. The algorithm then randomly chooses k points in that vector space, these point serve as the initial centers of the clusters. Afterwards all objects are each assigned to center they are closest to. Usually the distance measure is chosen by the user and determined by the learning task. After that, for each cluster a new center is computed by averaging the feature vectors of all objects assigned to it. The process of assigning objects and recomputing centers is repeated until the process converges. The algorithm can be proven to converge after a finite number of iterations. Several tweaks concerning distance measure, initial center choice and computation of new average centers have been explored, as well as the estimation of the number of clusters k. Yet the main principle always remains the same. In this project we will discuss about K-means clustering algorithm, implementation and its application to the problem of unsupervised learning
- 3. 3 Contents Abstract………………………………………………………………………....1 1. Introduction……………………………………………………………...…3 2. The k-means algorithm…………………………..........................................4 3. How the k-mean clustering algorithm works………………………….…...5 4. Task Formulation…………………………………………………………..6 4.1 K-means implementation………………………………………….…6 4.2 Estimation of parameters of a Gaussian mixture…………………….8 4.3 Unsupervised learning………………………………………………..9 5. Limitations………………………………………………………………..13 6. Difficulties with k-means…………………………………………………14 7. Available software……………………………………………………...…15 8. Applications of the k-Means Clustering Algorithm………………………15 9. Conclusion……………………………………………………………...…16 References…………………………………………………………………......17
- 4. 4 The k-means Algorithm 1 Introduction In this project, we describe the k-means algorithm, a straightforward and widely-used clustering algorithm. Given a set of objects (records), the goal of clustering or segmentation is to divide these objects into groups or “clusters” such that objects within a group tend to be more similar to one another as compared to objects belonging to different groups. In other words, clustering algorithms place similar points in the same cluster while placing dissimilar points in different clusters. Note that, in contrast to supervised tasks such as regression or classification where there is a notion of a target value or class label, the objects that form the inputs to a clustering procedure do not come with an associated target. Therefore clustering is often referred to as unsupervised learning. Because there is no need for labelled data, unsupervised algorithms are suitable for many applications where labeled data is difficult to obtain. Unsupervised tasks such as clustering are also often used to explore and characterize the dataset before running a supervised learning task. Since clustering makes no use of class labels, some notion of similarity must be defined based on the attributes of the objects. The definition of similarity and the method in which points are clustered differ based on the clustering algorithm being applied. Thus, different clustering algorithms are suited to different types of data sets and different purposes. The “best” clustering algorithm to use therefore depends on the application. It is not uncommon to try several different algorithms and choose depending on which is the most useful. The k-means algorithm is a simple iterative clustering algorithm that partitions a given dataset into a user-specified number of clusters, k. The algorithm is simple to implement and run, relatively fast, easy to adapt, and common in practice. It is historically one of the most important algorithms in data mining. Historically, k-means in its essential form has been discovered by several researchers across different disciplines, most notably Lloyd (1957,1982), Forgey (1965), Friedman and Rubin(1967), and McQueen(1967). A detailed history of k-means along with descriptions of several variations are given in Jain and Dubes. Gray and Neuhoff provide a nice historical background for k-means placed in the larger context of hill-climbing algorithms. In the rest of this project, we will describe how k-means works, discuss the limitations of k-means, difficulties and some applications of this algorithm.
- 5. 5 2 The k-means algorithm The k-means algorithm applies to objects that are represented by points in a 𝑑-dimensional vector space. Thus, it clusters a set of 𝑑-dimensional vectors, 𝐷 = {𝑥𝑖|𝑖 = 1,. .. , 𝑁} where 𝑥𝑖 ∈ ℜ 𝑑 denotes the ith object or “data point”. As discussed in the introduction, k-means is a clustering algorithm that partitions D intoｋclusters of points. That is, the k-means algorithm clusters all of the data points in 𝐷 such that each point 𝑥𝑖 falls in one and only one of the 𝑘 partitions. One can keep track of which point is in which cluster by assigning each point a cluster ID. Points with the same cluster ID are in the same cluster, while points with different cluster IDs are in different clusters. One can denote this with a cluster membership vector m of length N, where 𝑚𝑖 is the cluster ID of 𝑥𝑖. The value ofｋ is an input to the base algorithm. Typically, the value forｋ is based on criteria such as prior knowledge of how many clusters actually appear in 𝐷, how many clusters are desired for the current application, or the types of clusters found by exploring/experimenting with different values of 𝑘. How 𝑘 is chosen is not necessary for understanding how k-means partitions the dataset 𝐷, and we will discuss how to choose 𝑘 when it is not pre-specified in a later section. In k-means, each of the 𝑘 clusters is represented by a single point in ℜ 𝑑 . Let us denote this set of cluster representatives as the set 𝐶 = {𝑐𝑗|𝑗 = 1, .. ., 𝑘}. These 𝑘 cluster representatives are also called the cluster means or cluster centroids. In clustering algorithms, points are grouped by some notion of “closeness” or “similarity.” In k-means, the default measure of closeness is the Euclidean distance. In particular, one can readily show that k-means attempts to minimize the following non-negative cost function: ∑ (𝑎𝑟𝑔𝑚𝑖𝑛𝑗 ||𝑥𝑖 − 𝑐𝑗||2 2𝑁 𝑖=1 (1) In other words, k-means attempts to minimize the total squared Euclidean distance between each point 𝑥𝑖 and its closest cluster representative 𝑐𝑗 . Equation 1 is often referredto as the k-means objective function.
- 6. 6 3 How the k-mean clustering algorithm works Here is step by step k-means clustering algorithm： K -means clustering algorithm flowchart Step1. Begin with a decision on the value of k =number of clusters Step2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: 1. Take the first k training sample as single-element clusters. 2. Assign each of the remaining(N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
- 7. 7 If the number of data is less than the number of cluster then we assign each data as the centroid of the cluster. Each centroid will have a cluster number. If the number of data is bigger than the number of cluster, for each data, we calculate the distance to all centroid and get the minimum distance. This data is said belong to the cluster that has minimum distance from this data. Since we are not sure about the location of the centroid, we need to adjust the centroid location based on the current updated data. Then we assign all the data to this new centroid. This process is repeated until no data is moving to another cluster anymore. Mathematically this loop can be proved to be convergent. The convergence will always occur if the following condition satisfied: 1. Each switch in step 2 the sum of distances from each training sample to that training sample’s group centroid is decreased. 2. There are only finitely many partitions of the training examples into k clusters. 4 Task Formulation 4.1 K-means implementation To implement the K-means clustering algorithm we have to follow the description given below: Tasks: 1. Download test data data.mat, display them using ppatterns(). The file data.mat contains single variable X - 2×N matrix of 2D points. 2. Run the algorithm. In each iteration, display locations of means μj and current classification of the test data. To display the classification, use again the ppatterns function. 3. In each iteration, plot the average distance between points and their respective closest means μj. 4. Experiment with diferent number K of means, eg. K = 2,3,4. Execute the algorithm repeatedly, initialise the mean values μj with random positions. Use the function rand.
- 8. 8
- 9. 9 4.2 Estimation of parameters of a Gaussian mixture Let us assume that the distribution of our data is a mixture of three gaussians: 𝑝(𝑥) = 𝑆𝑢𝑚3 𝑗=1 𝑃(𝑗) 𝑁( 𝑥 | 𝜇 𝑗, 𝛴𝑗) , where N(μj ,Σj) denotes a normal distribution with mean value μ j and covariance Σ j. P(j) denotes the weight of j-th gaussian within the mixture. The task is, for given input data x1 , x2 , ... , xN, to estimate the mixture parameters μ j , Σ j , P(j) . Tasks: 1. In each iteration of the implemented k-means algorithm, reestimate means μ j and covariances Σ j using the maximal likelihood method. P(j) will be the relative number (percentage) of data points classified to j-th cluster. 2. In each iteration, plot the total likelihood L of estimated parameters μ j , Σ j , P(j) :
- 10. 10 4.3 Unsupervised learning Apply the K-means clustering to the problem of "unsupervised learning". The input consists of images of three letters, H, L, T. It is not known, which letter is shown in which images. The task is to classify the images into three classes. The images will be described by the two usual measurements: x = (sum of pixel intensities in the left half of the image) - (sum of pixel intensities in the right half of the image) y = (sum of pixel intensities in the upper half of the image) - (sum of pixel intensities in the lower half of the image) Tasks: 1. Download the images of letters image_data.mat, compute measurements x and y. 2. Using the k-means method, classify the images into three classes. In each iteration, display the means μj , current classification, and the likelihood L . 3. After the iteration stops, compute and display the average image of each of the three classes. To display the final classification, you can use show_class function.
- 11. 11
- 12. 12 Visualisation of classification by show_class: Class 1
- 13. 13 Class 2 Class 3
- 14. 14 5 Limitations The greedy-descent nature of k-means on a non-convex cost implies that the convergence is only to a local optimum, and indeed the algorithm is typically quite sensitive to the initial centroid locations. In other words, initializing the set of cluster representatives 𝐶 differently can lead to very different clusters, even on the same dataset 𝐷. A poor initialization can lead to very poor clusters. The local minima problem can be countered to some extent by running the algorithm multiple times with different initial centroids and then selecting the best result, or by doing limited local search about the converged solution. Other approaches include methods that attempts to keep k-means from converging to local minima. There are also a list of different methods of initialization, as well as a discussion of other limitations of k-means. As mentioned, choosing the optimal value of 𝑘 may be difficult. If one has knowledge about the dataset, such as the number of partitions that naturally comprise the dataset, then that knowledge can be used to choose 𝑘. Otherwise, one must use some other criteria to choose 𝑘, thus solving the model selection problem. One naive solution is to try several different values of 𝑘 and choose the clustering which minimizes the k-means objective function (Equation 1). Unfortunately, the value of the objective function is not as informative as one would hope in this case. For example, the cost of the optimal solution decreases with increasing 𝑘 till it hits zero when the number of clusters equals the number of distinct data points. This makes it more difficult to use the objective function to (a) directly compare solutions with different numbers of clusters and (b) to find the optimum value of 𝑘. Thus, if the desired 𝑘 is not known in advance, one will typically run k-means with different values of 𝑘, and then use some other, more suitable criterion to select one of the results. For example, SAS uses the cube-clustering-criterion, while X-means adds a complexity term (which increases with 𝑘) to the original cost function (Eq. 1) and then identifies the k which minimizes this adjusted cost. Alternatively, one can progressively increase the number of clusters, in conjunction with a suitable stopping criterion. Bisecting k-means achieves this by first putting all the data into a single cluster, and then recursively splitting the least compact cluster into two using 2-means. The celebrated LBG algorithm used for vector quantization doubles the number of clusters till a suitable code-book size is obtained. Both these approaches thus alleviate the need to know k beforehand.
- 15. 15 6 Difficulties with k-means k-means suffers from several other problems that can be understood by first noting that the problem of fitting data using a mixture of k Gaussians with identical, isotropic covariance matrices, (∑ = σ2 I) where I is the identity matrix, results in a “soft” version of k-means. More precisely, if the soft assignments of data points to the mixture components of such a model are instead hardened so that each data point is solely allocated to the most likely component, then one obtains the k-means algorithm. From this connection it is evident that k-means inherently assumes that the dataset is composed of a mixture of k balls or hyperspheres of data, and each of the k clusters corresponds to one of the mixture components. Because of this implicit assumption, k-means will falter whenever the data is not well described by a superposition of reasonably separated spherical Gaussian distributions. For example, k-means will have trouble if there are non-convex shaped clusters in the data. This problem may be alleviated by rescaling the data to “whiten” it before clustering, or by using a different distance measure that is more appropriate for the dataset. For example, information-theoretic clustering uses the KL-divergence to measure the distance between two data points representing two discrete probability distributions. It has been recently shown that if one measures distance by selecting any member of a very large class of divergences called Bregman divergences during the assignment step and makes no other changes, the essential properties of k-means, including guaranteed convergence, linear separation boundaries and scalability, are retained. This result makes k-means effective for a much larger class of datasets so long as an appropriate divergence is used. Another method of dealing with non-convex clusters is by pairing k-means with another algorithm. For example, one can first cluster the data into a large number of groups using k-means. These groups are then agglomerated into larger clusters using single link hierarchical clustering, which can detect complex shapes. This approach also makes the solution less sensitive to initialization, and since the hierarchical method provides results at multiple resolutions, one does not need to worry about choosing an exact value for k either; instead, one can simply use a large value for k when creating the initial clusters. The algorithm is also sensitive to the presence of outliers, since “mean” is not a robust statistic. A preprocessing step to remove outliers can be helpful. Post-processing the results, for example to eliminate small clusters, or to merge close clusters into a large cluster, is also desirable. Another potential issue is the problem of “empty” clusters . When running k-means, particularly with large values of k and/or when data resides in very high dimensional space, it is possible that at some point of execution, there exists a cluster representative cj such that all points xj in D are closer to some other cluster representative that is not cj . When points in D are assigned to
- 16. 16 their closest cluster, the jth cluster will have zero points assigned to it. That is, cluster j is now an empty cluster. The standard algorithm does not guard against empty clusters, but simple extensions (such as reinitializing the cluster representative of the empty cluster or “stealing” some points from the largest cluster) are possible. 7 Available software Because of the k-means algorithm’s simplicity, effectiveness, and historical importance, software to run the k-means algorithm is readily available in several forms. It is a standard feature in many popular data mining software packages. For example, it can be found in Weka or in SAS under the FASTCLUS procedure. It is also commonly included as add-ons to existing software. For example, several implementations of k-means are available as parts of various toolboxes in Matlab. k-means is also available in Microsoft Excel after adding XL Miner. Finally, several stand-alone versions of k-means exist and can be easily found on the Internet. The algorithm is also straightforward to code, and the reader is encouraged to create their own implementation of k-means as an exercise. 8 Applications of the k-Means Clustering Algorithm Briefly, optical character recognition, speech recognition, and encoding/decoding as example applications of k-means. However, a survey of the literature on the subject offers a more in depth treatment of some other practical applications, such as "data detection … for burst-mode optical receiver[s]", and recognition of musical genres. Researchers describe "burst-mode data-transmission systems," a "significant feature of burst-mode data transmissions is that due to unequal distances between" sender and receivers, "signal attenuation is not the same" for all receivers. Because of this, "conventional receivers are not suitable for burst-mode data transmissions." The importance, they note, is that many "high-speed optical multi-access network applications, [such as] optical bus networks [and] WDMA optical star networks" can use burst-mode receivers. In their paper, they provide a "new, efficient burst-mode signal detection scheme" that utilizes "a two-step data clustering method based on a K-means algorithm." They go on to explain that "the burst-mode signal detection
- 17. 17 problem" can be expressed as a "binary hypothesis," determining if a bit is 0 or 1. Further, although they could use maximum likelihood sequence estimation (MLSE) to determine the class, it "is very computationally complex, and not suitable for high-speed burst-mode data transmission." Thus, they use an approach based on k-means to solve the practical problem where simple MLSE is not enough. 9 Conclusion This project tried to explain about K-means clustering algorithm and its application to the problem of un supervised learning. The k-means algorithm is a simple iterative clustering algorithm that partitions a dataset into k clusters. At its core, the algorithm works by iterating over two steps: 1) clustering all points in the dataset based on the distance between each point and its closest cluster representative, and 2) re-estimating the cluster representatives. Limitations of the k-means algorithm include the sensitivity of k-means to initialization and determining the value of k. Despite its drawbacks, k-means remains the most widely used partitional clustering algorithm in practice. The algorithm is simple, easily understandable and reasonably scalable, and can be easily modified to deal with different scenarios such as semi-supervised learning or streaming data. Continual improvements and generalizations of the basic algorithm have ensured its continued relevance and gradually increased its effectiveness as well.
- 18. 18 References 1. http://www.ideal.ece.utexas.edu/papers/km.pdf 2. http://www.science.uva.nl/research/ias/alumni/m.sc.theses/theses/NoahLaith.doc 3. http://cw.felk.cvut.cz/cmp/courses/ae4b33rpz/Labs/kmeans/index_en.html
- 19. 19 Matlab codes for unsupervised learning task clear;close all; load('data.mat'); % X = 2x140 %% cast 1 model = kminovec(X, 4, 10, 1); %% cast 2 % clear Gmodel.Mean = [-2,1;1,1;0,-1]'; Gmodel.Cov (:, :, 1) = [ 0.1 0; 0 0.1]; Gmodel.Cov (:, :, 2) = [ 0.3 0; 0 0.3]; Gmodel.Cov (:, :, 3) = [ 0.01 0; 0 0.5]; Gmodel.Prior = [0.4;0.4;0.2]; gmm = gmmsamp(Gmodel, 100); figure(gcf);clf; ppatterns(gmm.X, gmm.y); axis([-3 3 -3 3]); model = kminovec(gmm.X, 3, 10, 1, gmm); figure(gcf);plot(model.L); %% cast 3 data = load('image_data.mat'); for i = 1:size(data.images, 3) % soucet sum leva - prava cast obrazku pX(i) = sum(sum(data.images(:, 1:floor(end/2) , i))) ... - sum(sum(data.images(:, (floor(end/2)+1):end , i))); % soucet sum horni - dolni pY(i) = sum(sum(data.images(1:floor(end/2),: , i))) ... - sum(sum(data.images((floor(end/2)+1):end , :, i))); end model = kminovec([pX;pY], 3, 10, 1); show_class(data.images, model.class'); %% d model = struct('Mean',[-2 3; 5 8],'Cov',[1 0.5],'Prior',[0.4 0.6;0]); figure; hold on; plot([-4:0.1:5], pdfgmm([-4:0.1:5],model),'r'); sample = gmmsamp(model,500); [Y,X] = hist(sample.X,10); bar(X,Y/500);

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