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Linear and nonlinear models for time series analysis and prediction are well-established. Clustering methods have also been applied to this area. This paper explores a framework that can be used to cluster time series data. The range of values of a time series is clustered. Then the time series is clustered by data windows that flow into the initial set of value clusters. This allows predictive temporal...
In composite event detection systems such as fire alarms, the two foremost goals are speed and accuracy. One way to achieve these goals is by performing data aggregation at central nodes. This helps reduce energy consumption and redundancy. In this paper we present a new hybrid approach that involves the use of k-means algorithm with neural networks, an efficient supervised learning algorithm that...
The identification of predictive biomarkers of complex disease with robustness and specificity is an ongoing challenge. Gene expressions provide information on how the cell reacts to a particular state and the relationship of genes may lead to novel information. A network-based approach integrating expression data with protein-protein interaction network can be used to identify gene-subnetwork biomarkers...
Forecasting solar power generated from photovoltaic systems at different time intervals is necessary for ensuring reliable and economic operation of the electricity grid. In this paper, we study the application of neural networks for predicting the next day photovoltaic power outputs in 30 minutes intervals from the previous values, without using any exogenous data. We propose three different approaches...
In this article the novel clustering and regression methods TEDACluster and TEDAPredict methods are described additionally to recently proposed evolving classifier TEDAClass. The algorithms for classification, clustering and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive processing of...
This paper presents a novel RBF-like fast dynamically Evolving Spiking Neural classifier (ESNC). The trained feed-forward SNN consists of three layers of spiking neurons: an encoding layer which temporally encodes real valued features into spatio-temporal spike patterns, a hidden layer of dynamically grown and pruned neurons which perform spatiotemporal clustering, and an evolving output layer for...
Data stream clustering aims at studying large volumes of data that arrive continuously and the objective is to build a good clustering of the stream, using a small amount of memory and time. Visualization is still a big challenge for large data streams. In this paper we present a new approach using a hierarchical and topological structure (or network) for both clustering and visualization. The topological...
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