We propose a novel accelerated exact k-means algorithm, which performs better than the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3× faster. We also propose a general improvement of existing state-of-the-art accelerated exact k-means algorithms through better estimates of the distance bounds used to reduce the number of distance calculations, and g...

We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without s...

Finding discords in time series database is an important problem in the last decade due to its variety of real-world applications, including data cleansing, fault diagnostics, and financial data analysis. The best known approach to our knowledge is HOT SAX technique based on the equiprobable distribution of SAX representations of time series. This characteristic, however, is not preserved in th...

In order to provide an efficient conversion and utilization of solar power, solar radiation datashould be measured continuously and accurately over the long-term period. However, the measurement ofsolar radiation is not available to all countries in the world due to some technical and fiscal limitations. Hence,several studies were proposed in the literature to find mathematical and physical mod...

This paper focuses on the enhanced initial centroids for the K-means algorithm. The original kmeans is using the random choice of init ial seeds which is a major limitation of the orig inal K-means algorithm because it produces less reliab le result of clustering the data. The enhanced method of the k-means algorithm includes the computation of the weighted mean to improve the centroids initial...

This paper proposes a fast k-means algorithm for graphs based on Elkan’s k-means for vectors. To accelerate the k-means algorithm for graphs without trading computational time against solution quality, we avoid unnecessary graph distance calculations by exploiting the triangle inequality of the underlying distance metric. In experiments we show that the accelerated k-means for graphs is faster ...

The K-Means Clustering Approach is one of main algorithms in the literature of Pattern recognition and Machine Learning. Yet, due to the random selection of cluster centers and the adherence of results to initial cluster centers, the risk of trapping into local optimality ever exists. In this paper, inspired by a genetic algorithm which is based on the K-means method , a new approach is develop...

and showed the solution G is the leading eigenvectors of the symmetric positive semi definite matrix K. When K = AA> (sample covariance matrix) with A = [x1, ...,xm], xi ∈ Rn, those eigenvectors form a basis to a k-dimensional subspace of Rn which is the closest (in L2 norm sense) to the sample points xi. The axes (called principal axes) g1, ...,gk preserve the variance of the original data in ...

Despite its simplicity and its linear time, a serial K-means algorithm's time complexity remains expensive when it is applied to a problem of large size of multidimensional vectors. In this paper we show an improvement by a factor of O(K/2), where K is the number of desired clusters, by applying theories of parallel computing to the algorithm. In addition to time improvement, the parallel versi...

In this note, we introduce a new algorithm to deal with finite dimensional clustering with errors in variables. The design of this algorithm is based on recent theoretical advances (see Loustau (2013a,b)) in statistical learning with errors in variables. As the previous mentioned papers, the algorithm mixes different tools from the inverse problem literature and the machine learning community. ...