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A computer program was applied to cluster indexing terms into field-of-interest categories, defined by responses of staff members of a personnel research laboratory. This provides a practical scheme for document classification. The method clusters successively pairs of topics that have the highest probability of being marked together by the scientist as both being of interest or neither one of interest. Ten fields of interest related to the group mission were identified by this hierarchal grouping. (Author).
At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review of the Japanese-language results on clustering, review of the Russian-language results on clustering and multidimensional scaling, practical advances, and significance tests.
This work explores several approaches to the classification of complex data types, including high-dimensional, spatial, and time series data. It contains three main sections, each demonstrating a different application area. In Chapter 2, we explore the behavior of wind speed over time, using the Eastern Wind Dataset published by the National Renewable Energy Laboratory. To assess differences in intra-day time series, we propose a functional distance measure, the band distance, which extends the band depth of Lopez-Pintado and Romo (2009). This measure emphasizes the shape of time series or functional observations relative to other members of a dataset, and allows clustering of observations without reliance on pointwise Euclidean distance. We also examine adjustments for seasonality and representations in the time-frequency domain. We demonstrate the utility of the new method in simulation studies and an application to the MOST power grid algorithm. Chapter 3 presents multivariate time series analysis motivated by applications in biophysics. Electric Cell-substrate Impedance Sensing measures the electrical behavior of a cell culture exposed to an alternating current; we can obtain linked time series by observing the changes in impedance over time at various AC frequencies. We explore a space of features derived from these time courses, toward classification of different types of cells based on their electrical behavior. In Chapter 4 we perform an exploratory analysis of a dataset provided by the Regional Transit Service of Rochester, NY, describing ridership on public buses. We describe the overall behavior of bus ridership, and proceed to a stop-level analysis. By examining daily patterns of ridership volume, we can find clusters of similar stops, and compare this form of similarity to other measures of stop similarity based on location and route information.
The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website