A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages ...

In this paper we present an algorithm for finding the k highest-ranked (or Top-k) answers in a distributed network. A Top-K query returns the subset of most relevant answers, in place of all answers, for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the recall and the precision of the answer-set, such that the user is not ov...

In this paper we investigate the top-k-selection problem, i.e. determine the largest, second largest, ..., and the k-th largest elements, in the dynamic data model. In this model the order of elements evolves dynamically over time. In each time step the algorithm can only probe the changes of data by comparing a pair of elements. Previously only two special cases were studied [2]: finding the l...

This paper presents a novel approach for accelerating the top-k heavy hitters query in data streams using Field Programmable Gate Arrays (FPGAs). Current hardware acceleration approaches rely on direct and strict mapping of software algorithms into hardware, limiting their performance practicality due to lack optimizations at an algorithmic level. The presented optimizes well-known algorithm by...

In this demonstration paper, we present gRecs, a system for group recommendations that follows a collaborative strategy. We enhance recommendations with the notion of support to model the confidence of the recommendations. Moreover, we propose partitioning users into clusters of similar ones. This way, recommendations for users are produced with respect to the preferences of their cluster membe...