Abstract In Content-Centric Networks (CCNs) as a possible future Internet, new kinds of
attacks and security challenges–from Denial of Service (DoS) to privacy attacks–will arise …

Abstract The “Big Data” era has arisen, driven by the increasing availability of data from
multiple sources such as social media, online transactions, network sensors or mobile …

In this work, we proposed two k-means-clustering-based algorithms to mitigate the fiber
nonlinearity for 64-quadrature amplitude modulation (64-QAM) signal, the training-sequence …

The clustering of web search results–or web document clustering–has become a very
interesting research area among academic and scientific communities involved in …

Clustering a large volume of data in a distributed environment is a challenging issue. Data
stored across multiple machines are huge in size, and solution space is large. Genetic …

Cluster analysis is one important field in pattern recognition and machine learning,
consisting in an attempt to distribute a set of data patterns into groups, considering only the …

Clustering is the technique used to partition the homogenous data, where the data are
grouped together. In order to improve the clustering accuracy, the adaptive dynamic …

Personalized e-learning based on recommender system is recognized as one of the most
interesting research field in the education and teaching in this last decade, since, the …

Dealing with distributed data is one of the challenges for clustering, as most clustering
techniques require the data to be centralized. One of them, k-means, has been elected as …

The recent growing size of datasets requires scalability of data mining algorithms, such as
clustering algorithms. The MapReduce programing model provides the scalability needed …