The correlative change analysis of state parameters can provide powerful technical supports
for safe, reliable, and high-efficient operation of the power transformers. However, the …

Determining valid clustering is an important research problem. This problem becomes
complex if the underlying data has inherent uncertainties. The work presented in this paper …

This work addresses the problem of clustering large uncertain graphs. The data is
represented as a graph where the proposed solution uses the neighborhood information for …

Clustering is a machine learning task to group similar objects in coherent sets. These groups
exhibit similar behavior with-in their cluster. With the exponential increase in the data …

Reverse k-nearest neighbor (R k NN R k NN) query on graphs returns the data objects that
take a specified query object q as one of their k-nearest neighbors. It has significant …

Probabilistic graphs have gained much interest in the data mining community since the big
data revolution. Graph clustering is a widely used technique in exploratory data analysis …

Graphs are commonly used to express the communication of various data. Faced with
uncertain data, we have probabilistic graphs. As a fundamental problem of such graphs …

Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a
large amount of uncertain data, eg, RFID data, sensor data, real-time video data, has been …

This paper introduces a novel model for spectral clustering to solve the problem of poor
connectivity among points within the same cluster as this can negatively impact the …

Wireless sensor networks (WSNs) often suffer from the disrupted connectivity due to
unpredictable wireless channels, early depletion of node energy, and physical tampering by …