Data Clustering

By Charu C. Aggarwal, Chandan K. Reddy. 2013

403 citations recorded by the source corpus.

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering processincluding how to verify the quality of the underlying clustersthrough supervision, human intervention, or the automated generation of alternative clusters.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d366e956e36fa989c7a666183acf72582a24ab02

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Citations in the corpus

Competitive Training of Mixtures of Independent Deep Generative Models (2018, background): “ces for our work are the literature on clustering, mixture of experts, causality, and implicit generative models. Clustering is a cornerstone of unsupervised learning and the literature is vast, see [Aggarwal and Reddy, 2013] for a recent overview. Kummerfeld and Ramsey [2016] focused on the inferen”
Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors (2019, methodology): “The density-based spatial clustering applications with noise (DBSCAN) is by far one of the most popular density-based methods for clustering (Ester et al., 1996; Aggarwal and Reddy, 2013).”
Temporal Relational Ranking for Stock Prediction (2018, background): “ory state, LSTM is capable of capturing the long-term dependency in the sequential data. 2.2 Graph-based Learning Graph-basedlearninghasbeenappliedtovariousmachinelearningtaskstoutilizeentityrelations[2, 26, 41]. The general problem setting is to learn a prediction function yˆ = f (x), which maps an”