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De Soete G. and Carroll, J. (1994) K-Means Clustering in a Low-Dimensional Euclidean Space. In: Diday, E., Lechevallier, Y., Schader, M., Bertrand, P. and Burtschy, B., Eds., New Approaches in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-51175-2_24

has been cited by the following article:

• TITLE: Prediction of Solar Radiation Using Data Clustering and Time-Delay Neural Network

  AUTHORS: Chee Keong Chan, Yi Hong Ler

  KEYWORDS: Prediction, Clustering, Neural Networks, Artificial Intelligence

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.12, December 26, 2018

  ABSTRACT: In this paper, a combination of data clustering and artificial intelligence techniques are used to predict incoming solar radiation on a daily basis. The data clustering technique known as Perceptually Important Points is proposed, where time-series data is grouped into clusters separated by key characteristic points, which are later used as training data for an artificial neural network. The type of network used is known as a Focused Time-Delay Neural Network, and an analysis of the data is performed using the Mean Absolute Percentage Error scheme.