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Clustering high dimensional data

ACM SIGKDD Explorations Newsletter, 2014
The goal of this position paper is to contribute to a clear understanding of the commonalities and differences between subspace clustering and text clustering. Often text data is foisted as an ideal fit for subspace clustering due to its high dimensional nature and sparsity of the data. Indeed, the areas of subspace clustering and text clustering share
Hans-Peter Kriegel, Eirini Ntoutsi
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Clustering High-Dimensional Data

2023
Clustering algorithms have been adapted or specifically designed for high-dimensional data where many attributes might be just noise such that patterns can be identified only in appropriate combinations of attributes and would be obfuscated by noise otherwise.
Houle, Michael E., Kiermeier, Marie, Zimek, Arthur   +2 more
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High-dimensional Data

2019
We have a dataset that is a collection of d-dimensional vectors. This chapter introduces the nasty tricks that such data can play. A dataset like this is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean).
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Forecasting high-dimensional data

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010
We propose a method for forecasting high-dimensional data (hundreds of attributes, trillions of attribute combinations) for a duration of several months. Our motivating application is guaranteed display advertising, a multi-billion dollar industry, whereby advertisers can buy targeted (high-dimensional) user visits from publishers many months or even ...
Deepak Agarwal, Datong Chen, Long-ji Lin, Jayavel Shanmugasundaram, Erik Vee   +4 more
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Clustering High-Dimensional Data

2015
This chapter introduces the task of clustering, concerning the definition of a structure aggregating the data, and the challenges related to its application to the unsupervised analysis of high-dimensional data. In the recent literature, many approaches have been proposed for facing this problem, as the development of efficient clustering methods for ...
MASULLI, FRANCESCO, ROVETTA, STEFANO
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Clustering high-dimensional data

ACM Transactions on Knowledge Discovery from Data, 2009
As a prolific research area in data mining, subspace clustering and related problems induced a vast quantity of proposed solutions. However, many publications compare a new proposition—if at all—with one or two competitors, or even with a so-called “naïve” ad hoc solution, but fail to clarify the exact problem definition.
Hans-Peter Kriegel, Peer Kröger, Arthur Zimek   +2 more
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High-dimensional Data Visualization

2007
One of the biggest challenges in data visualization is to find general representations of data that can display the multivariate structure of more than two variables. Several graphic types like mosaicplots, parallel coordinate plots, trellis displays, and the grand tour have been developed over the course of the last three decades.
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High-Dimensional Data Classification

2014
Recently, high-dimensional classification problems have been ubiquitous due to significant advances in technology. High dimensionality poses significant statistical challenges and renders many traditional classification algorithms impractical to use.
Vijay Pappu, Panos M. Pardalos
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High-dimensional data

American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Melvin Geubbelmans, Axel-Jan Rousseau, Dirk Valkenborg, Tomasz Burzykowski   +3 more
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HIGH DIMENSIONAL DATA ANALYSIS

COSMOS, 2005
We present two examples to show how the classical multivariate statistical approaches significantly lose efficiency or do not even work when dealing with high dimensional data analysis. These underline the importance and urgency of developing new theories to fit the urgent need of high dimensional data analysis.
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