Mixed datasets consist of both numeric and categorical attributes. Various K-means-based clustering algorithms have been developed to cluster these datasets. Generally, these algorithms use random partition as a starting point, which tend to produce different clustering results in different runs. This inconsistency of clustering results may lead to unreliable inferences from the data. A few initialization algorithms have been developed to compute initial partition for mixed datasets; however, they are either computationally expensive or they do not produce consistent clustering results in different runs. In this paper, we propose, initKmix, a novel approach to find initial partition for K-means-based clustering algorithms for mixed datasets. The initKmix is based on the experimental observations that (i) some data points in a dataset remain in the same clusters created by k-means-based clustering algorithm irrespective of the choice of initial clusters, and (ii) individual attribute information can be used to create initial clusters. In initKmix method, a k-means-based clustering algorithm is run many times, in each run one of the attribute is used to produce initial partition. The clustering results of various runs are combined to produce initial partition. This initial partition is then be used as a seed to a k-means-based clustering algorithm to cluster mixed data. The initial partitions produced by initKmix are always fixed, do not change over different runs or by changing the order of the data objects. Experiments with various categorical and mixed datasets showed that initKmix produced accurate and consistent results, and outperformed random initialization and other state-of-the-art initialization methods. Experiments also showed that K-means-based clustering for mixed datasets with initKmix outperformed many state-of-the-art clustering algorithms.
k-Harmonic means clustering algorithm addresses the random initial clusters problem by using a different cost function @cite_17 for numeric datasets. K-Harmonic means clustering algorithm clusters create more stable clusters as compared to K-means clustering algorithm with random initial clusters. Ahmad and Hashmi @cite_15 combine the distance measure and the definition of cluster centres for mixed datasets suggested by Ahmad and Dey @cite_28 with k-Harmonic clustering algorithm @cite_17 to develop the k-Harmonic clustering algorithm for mixed datasets. Their method is less sensitive to the choice of initial cluster centres. The standard deviation of clustering accuracy of this method is small as compared to the random initialization method. However, it does not give the same clustering results in different runs with different initial partitions.
@cite_28 @cite_15 @cite_17
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Display Omitted A K-Harmonic clustering algorithm for mixed data has been presented to reduce random initialization problem for partitional algorithms.The proposed clustering algorithm uses a distance measure developed for mixed datasets.The experiment results suggest that clustering results are quite insensitive to random initialization.The proposed algorithm performed better than other clustering algorithms for various datasets. K-means type clustering algorithms for mixed data that consists of numeric and categorical attributes suffer from cluster center initialization problem. The final clustering results depend upon the initial cluster centers. Random cluster center initialization is a popular initialization technique. However, clustering results are not consistent with different cluster center initializations. K-Harmonic means clustering algorithm tries to overcome this problem for pure numeric data. In this paper, we extend the K-Harmonic means clustering algorithm for mixed datasets. We propose a definition for a cluster center and a distance measure. These cluster centers and the distance measure are used with the cost function of K-Harmonic means clustering algorithm in the proposed algorithm. Experiments were carried out with pure categorical datasets and mixed datasets. Results suggest that the proposed clustering algorithm is quite insensitive to the cluster center initialization problem. Comparative studies with other clustering algorithms show that the proposed algorithm produce better clustering results. We propose a new class of center-based iterative clustering algorithms, K-Harmonic Means (KHMp), which is essentially insensitive to the initialization of the centers, demonstrated through many experiments. The insensitivity to initialization is attributed to a dynamic weighting function, which increases the importance of the data points that are far from any centers in the next iteration. The dependency of the K-Means’ and EM’s performance on the initialization of the centers has been a major problem. Many have tried to generate good initializations to solve the sensitivity problem. KHMp addresses the intrinsic problem by replacing the minimum distance from a data point to the centers, used in K-Means, by the Harmonic Averages of the distances from the data point to all centers. KHMp significantly improves the quality of clustering results comparing with both K-Means and EM. The KHMp algorithms have been implemented in both sequential and parallel languages and tested on hundreds of randomly generated datasets with different data distribution and clustering characteristics. In this paper we present a new clustering method based on k-means that have avoided alternative randomness of initial center. This paper focused on K-means algorithm to the initial value of the dependence of k selected from the aspects of the algorithm is improved. First,the initial clustering number is. Second, through the application of the sub-merger strategy the categories were combined.The algorithm does not require the user is given in advance the number of cluster. Experiments on synthetic datasets are presented to have shown significant improvements in clustering accuracy in comparison with the random k-means. In this paper, we present "k-means+ID3", a method to cascade k-means clustering and the ID3 decision tree learning methods for classifying anomalous and normal activities in a computer network, an active electronic circuit, and a mechanical mass-beam system. The k-means clustering method first partitions the training instances into k clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. To obtain a final decision on classification, the decisions of the k-means and ID3 methods are combined using two rules: 1) the nearest-neighbor rule and 2) the nearest-consensus rule. We perform experiments on three data sets: 1) network anomaly data (NAD), 2) Duffing equation data (DED), and 3) mechanical system data (MSD), which contain measurements from three distinct application domains of computer networks, an electronic circuit implementing a forced Duffing equation, and a mechanical system, respectively. Results show that the detection accuracy of the k-means+ID3 method is as high as 96.24 percent at a false-positive-rate of 0.03 percent on NAD; the total accuracy is as high as 80.01 percent on MSD and 79.9 percent on DED