The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...

In this paper, a novel self-adaptive fuzzy learning (SAFL) system is proposed for streaming data prediction. SAFL self-learns from streams predictive model composed of set prototype-based rules, with each which representing certain local distribution, and continuously self-evolves to follow the changing patterns in non-stationary environments. Unlike conventional evolving systems, both inferenc...

The weighted fuzzy c-mean clustering algorithm (WFCM) and weighted fuzzy c-mean-adaptive cluster number (WFCM-AC) are extension of traditional fuzzy c-mean algorithm to stream data clustering algorithm. Clusters in WFCM are generated by renewing the centers of weighted cluster by iteration. On the other hand, WFCM-AC generates clusters by applying WFCM on the data & selecting best K± initialize...

As a powerful tool for data streams processing, the vast majority of existing evolving intelligent systems (EISs) learn prediction models from in supervised manner. However, high-quality labelled can be difficult to obtain many real-world classification applications concerning streams, though unlabelled is plentiful. To overcome labelling bottleneck and construct stronger model, novel semi-supe...

Multidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier analysis to provide good learning and tracking abilities in both concept shift and concept drift.

Fine-grained allocation of compute resources, in terms of configurable clock speed of virtual machines, is essential for processing efficiency and resource utilization of data streaming applications. For a data streaming application, its processing speed is expected to approach the allocated bandwidth as much as possible. Automatic control technology is a feasible solution, but the plant model ...

Multiple radio access technologies (RATs) are available for the mobile users for the Internet connectivity. Traditional handover algorithms select the RAT based on the signal strength only. But quality of service (QoS) attributes are different for every application like VOIP requires lower network delay while video streaming application requires higher data rate. Therefore, these QoS parameters...

Fine-grained allocation of compute resources, in terms of configurable clock speed of virtual machines, is essential for processing efficiency and resource utilization of data streaming applications. For a data streaming application, its processing speed is expected to approach the allocated bandwidth as much as possible. Automatic control technology is a feasible solution, but the plant model ...