Results 71 to 80 of about 26,646,267 (236)
Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption
Entropy, 2020 Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects.
Krzysztof Gajowniczek +2 more
doaj +1 more source
Identifying Correlated Heavy-Hitters in a Two-Dimensional Data Stream [PDF]
, 2013 We consider online mining of correlated heavy-hitters from a data stream. Given a stream of two-dimensional data, a correlated aggregate query first extracts a substream by applying a predicate along a primary dimension, and then computes an aggregate ...
Lahiri, Bibudh +2 more
core
Solving $k$-means on High-dimensional Big Data
, 2015 In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement.
AK Jain +12 more
core +1 more source
Clustering categorical data streams
Journal of Computational Methods in Sciences and Engineering, 2011 In this paper, we propose an efficient clustering algorithm for analyzing categorical data streams. It has been proved that the proposed algorithm uses small memory footprints. We provide empirical analysis on the performance of the algorithm in clustering both synthetic and real data streams.
He, Zengyou +3 more
openaire +2 more sources
Scikit-Multiflow: A Multi-output Streaming Framework
, 2018 Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods ...
Abdessalem, Talel +3 more
core +1 more source
ACM SIGMOD Record, 2005 The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic.
Gaber, M. +2 more
openaire +3 more sources
Towards Meta-learning over Data Streams [PDF]
, 2014 Modern society produces vast streams of data. Many stream mining algorithms have been developed to capture general trends in these streams, and make predictions for future observations, but relatively little is known about which algorithms perform ...
Holmes, Geoffrey +3 more
core +1 more source
2011 Data Compression Conference, 2011 We present a general-purpose, loss less compressor for streaming data. This compressor is based on the deplump probabilistic compressor for batch data. Approximations to the inference procedure used in the probabilistic model underpinning deplump are introduced that yield the computational asyptotics necessary for stream compression. We demonstrate the
Bartlett, N, Wood, F
openaire +2 more sources
Error Metrics for Learning Reliable Manifolds from Streaming Data
, 2017 Spectral dimensionality reduction is frequently used to identify low-dimensional structure in high-dimensional data. However, learning manifolds, especially from the streaming data, is computationally and memory expensive.
Chandola, Varun +4 more
core +1 more source
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R
, 2017 In recent years, data streams have become an increasingly important area of research for the computer science, database and statistics communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non ...
Michael Hahsler +2 more
semanticscholar +1 more source