Results 121 to 130 of about 5,420 (166)
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AbstractReal‐time stream data processing has gained high importance with the rapid rise of big data trends in different areas such as social media, finance, business, science, and bioinformatics. Stream data can be characterized as fast, unstable, and big data sets.
Musa Milli, Hasan Bulut
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Online ensemble learning algorithm for imbalanced data stream
Applied Soft Computing, 2021Abstract In many practical applications, due to the inability to collect complete training data sets at one time, the adaptability of the classifier is poor. Online ensemble learning can better solve this problem. However, most of the data streams are imbalanced.
Hongle Du +4 more
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Quantum Online Streaming Algorithms with Logarithmic Memory
International Journal of Theoretical Physics, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kamil Khadiev, Aliya Khadieva
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An Improved Online Stream Data Clustering Algorithm
2012 Second International Conference on Business Computing and Global Informatization, 2012The stream data mining is a hot research topic in recent years. In order to improve the efficiency of stream data mining, this paper designs an online stream data clustering algorithm IStrAP. IStrAP considers the features of stream data, such as potentially infinity, rapidness, and inability to scan historical data repeatedly, and introduces a method ...
Lingjuan Li, Xiong Li
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Online Algorithms for Caching Multimedia Streams
2000We consider the problem of caching multimedia streams in the internet. We use the dynamic caching framework of Dan et al. and Hofmann et al.. We define a novel performance metric based on the maximum number of simultaneous cache misses, andp resent near-optimal on-line algorithms for determining which parts of the streams should be cachedat any point ...
Matthew Andrews, Kamesh Munagala
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A Novel HOSFS Algorithm for Online Streaming Feature Selection
2020 International Conference on System, Computation, Automation and Networking (ICSCAN), 2020In recent days, Data stream mining is important for many of the real time and IOT based applications. Online feature selection is the one big topic of data stream mining which attracted researchers with intensive interest. This technique reduces the dimensionality of the streaming features by excluding inappropriate and redundant features.
S. Sandhiya, U. Palani
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A new evolving clustering algorithm for online data streams
2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 2016In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations.
Bezerra, Clauber Gomes +3 more
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Online stream clustering using density and affinity propagation algorithm
2013 IEEE 4th International Conference on Software Engineering and Service Science, 2013In order to improve the data stream clustering accuracy and effectiveness, the paper proposes an efficient data stream clustering algorithm based on affinity propagation and density methods (APDenStream). The algorithm adopts online/offline two stage process framework, by using micro-cluster decay density to reflect the evolution information and using ...
null Jian-Peng Zhang +3 more
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On an Improved SPRINT Data Stream Online Classification Algorithm
Advanced Materials Research, 2013Aiming at the characteristics of data stream, the paper presents an incremental decision tree algorithm based on binary-attribute tree on the basis of SPRINT algorithm. The attribute set of this improved algorithm adopts the maximum entropy attribute classification and dynamic storage method of Bayesian method.
Hong Zhou +4 more
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Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search
International Journal of Pattern Recognition and Artificial Intelligence, 2019With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed.
Peng Zhan +5 more
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