Results 221 to 230 of about 5,816 (262)
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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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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 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.
Clauber Gomes Bezerra +3 more
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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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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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Algorithm for Online Detection of the Data Stream Based on Distance
2016To address the inaccuracy and high time complexity of traditional data stream mining technology, this paper introduces a new algorithm of date detection based on k-distance to pruning and comentropy to detect sliding windows. When the data fills the current window, the k-distance of the data is used to prune all data in the pruning time.
Liya Yu +3 more
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ACM Transactions on Mathematical Software, 2010
We present a novel, online algorithm for exact summation of a stream of floating-point numbers. By “online” we mean that the algorithm needs to see only one input at a time, and can take an arbitrary length input stream of such inputs while requiring only constant memory. By “exact” we mean that the sum of the internal array of our algorithm is exactly
Yongkang Zhu, Wayne B. Hayes
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We present a novel, online algorithm for exact summation of a stream of floating-point numbers. By “online” we mean that the algorithm needs to see only one input at a time, and can take an arbitrary length input stream of such inputs while requiring only constant memory. By “exact” we mean that the sum of the internal array of our algorithm is exactly
Yongkang Zhu, Wayne B. Hayes
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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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Rival Learner Algorithm with Drift Adaptation for Online Data Stream Regression
Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 2018Real-time extraction of meaningful data streams patterns is an increasingly important issue for machine learning and data mining communities. In this paper, we proposed a regression algorithm incremental on data streams which are infinite, high-speed and time-varying. The algorithm integrates two incremental model trees, global and local models.
Zhenwei Liao, Yongheng Wang
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