This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …

Clustering is an unsupervised learning technology, and it groups information (observations
or datasets) according to similarity measures. Developing clustering algorithms is a hot topic …

The processes of retrieving useful information from a dataset are an important data mining
technique that is commonly applied, known as Data Clustering. Recently, nature-inspired …

In the field of medical, each and every analysis is decisive as the study links to life of the
subject under observation. One of the most vital area in the field of medical is the healthcare …

In this paper, a brief survey of data preprocessing methods is presented. Specifically, the
data preprocessing methods used in the smart grid (SG) domain are surveyed. Also, with the …

Approximate similarity queries on high-dimensional vector indices have become the
cornerstone for many critical online services. An increasing need for more sophisticated …

Technological advancement has enabled us to store and process huge amount of data in
relatively short spans of time. The nature of data is rapidly changing, particularly its …

The genetic algorithm (GA), one of the best-known metaheuristic algorithms, has been
extensively utilized in various fields of management science, operational research, and …

Data center power consumption is among the largest commodity expenditures for many
organizations. Reduction of power used in cloud data centres with heterogeneous physical …

Big Data is usually defined by three characteristics called 3Vs (Volume, Velocity and
Variety). It refers to data that are too large, dynamic and complex. In this context, data are …