Information-theoretic K-means (Info-Kmeans) aims to cluster high-dimensional data, such as images featured by the bag-of-features (BOF) model, using K-means algorithm with KL-divergence as the distance. While research efforts along this line have shown promising results, a remaining challenge is to deal with the high sparsity of image data. Indeed, the centroids may contain many zero-value feat...

In this paper we propose the use of optimization based clustering algorithms to determine hierarchical multicast trees. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artificial and real-world databases are reported. We compare several ...

The proposed work represents ranking based method that improved K-means clustering algorithm performance and accuracy. In this we have also done analysis of K-means clustering algorithm, one is the existing Kmeans clustering approach which is incorporated with some threshold value and second one is ranking method which is weighted page ranking applied on K-means algorithm, in weighted page rank...

Through comparison and analysis of clustering algorithms, this paper presents an improved Kmeans clustering algorithm. Using genetic algorithm to select the initial cluster centers, using Z-score to standardize data, and take a new method to evaluate cluster centers, all this reduce the affect of isolated points, and improve the accuracy of clustering. Experiments show that the algorithm to fin...

Abstract. Unsupervised identification of groups in large data sets is important for many machine learning and knowledge discovery applications. Conventional clustering approaches (kmeans, hierarchical clustering, etc.) typically do not scale well for very large data sets. In recent years, data stream clustering algorithms have been proposed which can deal efficiently with potentially unbounded ...

The problem of identifying clusters from MIMO measurement data is addressed. Conventionally, visual inspection has been used for cluster identification, but this approach is impractical for a large amount of measurement data. For automatic clustering, the multipath component distance (MCD) is used to calculate the distance between individual multipath components estimated by a channel parameter...

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and design operation hydraulic structures. presented study proposes a new method for accurately estimating daily SSs using antecedent discharge sediment information. novel is develo...

Retrieving relevant text documents on a topic from a large document collection is a challenging task. Different clustering algorithms are developed to retrieve relevant documents of interest. Hierarchical clustering shows quadratic time complexity of O(n 2 ) for n text documents. K-means algorithm has a time complexity of O(n) but it is sensitive to the initial randomly selected cluster centers...