Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial settings and the large number of distance computations that it can require to converge, the K-means algorithm remains as one of the most popular clustering methods for massive data...

The k-means++ algorithm is the state of the art algorithm to solve k-Means clustering problems as the computed clusterings are O(log k) competitive in expectation. However, its seeding step requires k inherently sequential passes through the full data set making it hard to scale to massive data sets. The standard remedy is to use the k-means‖ algorithm which reduces the number of sequential rou...

This paper reflects the results of an implementation of the K-means algorithm on U.N survey data on people’s priorities, organized by country. The dataset includes 16 features for each country, with each feature corresponding to a different societal issue. Each country has a rating in the range of [0, 1] that indicates how important a particular feature or issue is to that country’s people– the...

In many modern applications from, for example, bioinformatics and computer vision, samples have multiple feature representations coming from different data sources. Multiview learning algorithms try to exploit all these available information to obtain a better learner in such scenarios. In this paper, we propose a novel multiple kernel learning algorithm that extends kernel k-means clustering t...

recent evidences suggest that multiple myeloma phenotypes (mmps) are involved in the infiltration of multiple myeloma-affected marrow foci. in this study, the effects of arsenic trioxide on the invasive and angiogenic phenotypes of multiple myeloma (mm) cell line were assessed on a dose-response and time-course basis. multiple myeloma cell line, karpas 707, was treated with step-wise elevated c...

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors...

In this paper, we introduce a new iterative algorithm for approximating a common solution of certain class of multiple-sets split variational inequality problems. The sequence of the proposed iterative algorithm is proved to converge strongly in Hilbert spaces. As application, we obtain some strong convergence results for some classes of multiple-sets split convex minimization problems.

Non-invasive blood glucose level (BGL) evaluation technology in skin mucus is a wearable stress-detection means to indicate the health status of live fish for compensating drawbacks using traditional invasive biochemical inspection. Nevertheless, commonly used methods cannot accurately obtain BGL variations owing influence an uncertain exudation rate, ambient effects, and individualized differe...