This paper presents the fourth participation of the SINAI group, University of Jaén, in the Photo Retrieval task at Image CLEF 2009. Our system uses only the text of the queries, and a clustering system (based on kmeans) that combines different approaches based on a different use of the cluster data of the queries. The official results shown that the combination between the title of each query ...

The Positron Emission Tomography (PET) scan image requires expertise in the segmentation where clustering algorithm plays an important role in the automation process. The algorithm optimization is concluded based on the performance, quality and number of clusters extracted. This paper is proposed to study the commonly used KMeans clustering algorithm and to discuss a brief list of toolboxes for...

The k-means algorithm is one of the most common clustering algorithms and widely used in data mining and pattern recognition. The increasing computational requirement of big data applications makes hardware acceleration for the kmeans algorithm necessary. In this paper, a coarse-grained Map-Reduce architecture is proposed to implement the kmeans algorithm on an FPGA. Algorithmic segmentation, d...

We present a new clustering algorithm that is based on searching for natural gaps in the components of the lowest energy eigenvectors of the Laplacian of a graph. In comparing the performance of the proposed method with a set of other popular methods (KMEANS, spectral-KMEANS, and an agglomerative method) in the context of the Lancichinetti-Fortunato-Radicchi (LFR) Benchmark for undirected weigh...

We present the results of several unsupervised algorithms tested on the MNIST database as well as techniques we used to improve the classification accuracy. We find that spiking neural network outperforms kmeans clustering and reaches the same level as the supervised SVM. We then discuss several inherent issues of unsupervised methods for the handwritten digit classfication problem and propose ...

This paper presents a modified RBF network with additional linear input connections together with a hybrid training algorithm. The training algorithm is based on kmeans clustering with square root updating method and Givens least squares algorithm with additional linear input connections features. Two real data sets have been used to demonstrate the capability of the proposed RBF network archit...

This paper proposes an efficient embedding method for scaling kernel k-means on cloud infrastructures. The embedding method allows for approximating the computation of the nearest centroid to each data instance and, accordingly, it eliminates the quadratic space and time complexities of the cluster assignment step in the kernel k-means algorithm. We show that the proposed embedding method is ef...

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combinati...

Selecting the initial clustering centers randomly will cause an instability final result, and make it easy to fall into local minimum. To improve the shortcoming of the existing kmeans clustering center selection algorithm, an optimized k-means algorithm for selecting initial clustering centers is proposed in this paper. When the number of the sample’s maximum density parameter value is not uni...

Clustering of relational data has so far received a lot less attention than classification of such data. In this paper we investigate a simple approach based on randomized propositionalization, which allows for applying standard clustering algorithms like KMeans to multi-relational data. We describe how random rules are generated and then turned into boolean-valued features. Clustering generall...