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Journal articles on the topic 'Real-time data stream mining'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Eiman, Alothali, Alashwal Hany, and Harous Saad. "Data stream mining techniques: a review." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 2 (2019): 728–37. https://doi.org/10.12928/TELKOMNIKA.v17i2.11752.

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A plethora of infinite data is generated from the Internet and other information sources. Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. However, data stream mining has different challenges making it different from traditional data mining. Recently, many studies have addressed the concerns on massive data mining problems and proposed several techniques that produce impressive results. In this paper, we review real time clustering and classification mining techniques fo

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Karan, Patel, Sakaria Yash, and Bhadane Chetashri. "Real Time Data Processing Frameworks." International Journal of Data Mining & Knowledge Management Process (IJDKP) 5, no. 5 (2019): 49–63. https://doi.org/10.5281/zenodo.3406010.

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On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being

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Raviteja, Mellacheruvu, Vanguru Venkata Varun Kumar Reddy, Shaik Aqibuddin, and Hareendra Sri Nag Nerusu. "Real-Time Event Detection in Social Media Streams using Stream Data Mining." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 1676–82. http://dx.doi.org/10.22214/ijraset.2023.55328.

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Abstract: The need for real-time event detection has grown rapidly in the modern world of instantaneous information transmission through social media. In order to implement real-time event detection inside the dynamic environment of social media streams, this research article provides a ground-breaking framework that harnesses the power of stream data mining techniques. The combination of three different stream data mining algorithms—Sliding Window Analysis, Burst Detection using K-Means, and Agglomerative Hierarchical Clustering—allows us to tackle this problem. Together, these algorithms mak

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Ramzan, Faisal, and Muawaz Ayyaz. "A COMPREHENSIVE REVIEW ON DATA STREAM MINING TECHNIQUES FOR DATA CLASSIFICATION; AND FUTURE TRENDS." EPH - International Journal of Science And Engineering 9, no. 3 (2023): 1–29. http://dx.doi.org/10.53555/ephijse.v9i3.201.

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Data Mining is a developing interdisciplinary control managing Data Reclamation and Data Stream Mining techniques, whose subject is gathering, overseeing, processing, breaking down, and visualizing the huge volume of organized or unstructured data. Data stream mining indicates how to look at Unknown patterns from a massive amount of data over algorithms. It has experienced quick improvement with significant progress in math, statistics, data science, and computer science domains. Data streams are commonly generated by various sources such as sensor networks, social media feeds, financial trans

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Mansour, ManaL, and Manal Abdullah. "Mining Techniques for Streaming Data." International Journal of Data Mining & Knowledge Management Process 12, no. 2 (2022): 1–14. http://dx.doi.org/10.5121/ijdkp.2022.12201.

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The huge explosion in using real time technology leads to infinite flow of data which known as data streams. The characteristics of streaming data require different techniques for processing due its volume, velocity and volatility, beside issues related to the limited storage capabilities. Hence, this research highlights the significant aspects to consider when building a framework for mining data streams. It reviews the methods for data stream summarizing and creating synopsis, and the approaches of processing these data synopses. The goal is to present a model for mining the streaming data w

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A Ravi Kishore, Dr Gururaj Murtugudde. "Secure And Adaptive Data Stream Mining For New Generation Big Data." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 2055–61. http://dx.doi.org/10.52783/tjjpt.v44.i4.1180.

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As the era of new-generation big data applications unfolds, the need for secure and adaptive data stream mining has become increasingly paramount. Evolving databases, characterized by ever-changing data streams and dynamic data distributions, present unique challenges and opportunities. This paper addresses the crucial intersection of security and adaptability in the context of data stream mining for new-generation big data. First, we delve into the evolving landscape of big data, where real-time data streams from diverse sources drive decision-making processes. Ensuring the privacy and securi

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Zhang, Yang, Simon Fong, Jinan Fiaidhi, and Sabah Mohammed. "Real-Time Clinical Decision Support System with Data Stream Mining." Journal of Biomedicine and Biotechnology 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/580186.

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This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare, diabetes diagnosis, and so forth. Most of the existing software technologies are case-based data mining systems. They only can analyze finite and structured data set and can only work well in their earl

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Joshi, Dhara, and Madhu Shukla. "An Ensemble Approach to Improve the Performance of Real Time Data Stream Classification." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 17749–54. https://doi.org/10.48084/etasr.8563.

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In the era of the Internet of Things (IoT), data stream mining has gained importance to make accurate and profitable decisions. Various techniques are used to gain insight into data streams, including classification, clustering, pattern mining, etc. Data are subject to changes over time. When this happens, predictive models that assume a static link between input and output variables may perform poorly or even degrade, which is called concept drift. This study proposes an ensemble architecture designed to improve performance and effectively detect concept drift in stream data classification. U

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Jyoti, Wagde*, and Deshkar Prarthana. "AN EFFICIENT INDEXED BASED CLASSIFICATION APPROACH FOR FEATURE EVOLVING DATA STREAMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 554–59. https://doi.org/10.5281/zenodo.49791.

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Today, rapid growth in hardware technology has provided a means to generate huge volume of data continuously. In most of the real time data stream application data usually reach very rapidly that flows continuously in real time environment .This incoming data streams comprises of several important and interesting patterns underneath. However, mining an essential data out from this data stream, has some major challenges such as infinite length, concept evolution and concept-drift .Earlier studies, carried till now, have been mainly focusing on building accurate classification model. But, keepin

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Kim, Jaein, and Buhyun Hwang. "Real-time stream data mining based on CanTree and Gtree." Information Sciences 367-368 (November 2016): 512–28. http://dx.doi.org/10.1016/j.ins.2016.06.018.

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Stahl, Frederic, Thien Le, Atta Badii, and Mohamed Medhat Gaber. "A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams." Information 12, no. 1 (2021): 24. http://dx.doi.org/10.3390/info12010024.

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This paper introduces a new and expressive algorithm for inducing descriptive rule-sets from streaming data in real-time in order to describe frequent patterns explicitly encoded in the stream. Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predict

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Stahl, Frederic, Thien Le, Atta Badii, and Mohamed Medhat Gaber. "A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams." Information 12, no. 1 (2021): 24. http://dx.doi.org/10.3390/info12010024.

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This paper introduces a new and expressive algorithm for inducing descriptive rule-sets from streaming data in real-time in order to describe frequent patterns explicitly encoded in the stream. Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predict

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Qiu, Yongxiao, Guanghui Du, and Song Chai. "A Novel Algorithm for Distributed Data Stream Using Big Data Classification Model." International Journal of Information Technology and Web Engineering 15, no. 4 (2020): 1–17. http://dx.doi.org/10.4018/ijitwe.2020100101.

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In order to solve the problem of real-time detection of power grid equipment anomalies, this paper proposes a data flow classification model based on distributed processing. In order to realize distributed processing of power grid data flow, a local node mining method and a global mining mode based on uneven data flow classification are designed. A data stream classification model based on distributed processing is constructed, then the corresponding data sequence is selected and formatted abstractly, and the local node mining method and global mining mode under this model are designed. In the

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Vanguru, Swapna, Anusha Merugu, and Y.Geetha Reddy. "Clustering Techniques for Streaming Dynamic Nature of Data." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 12 (2015): 2027–29. https://doi.org/10.5281/zenodo.14789769.

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Nowadays many applications are generating streaming data for an example real-time surveillance, internet traffic, sensor data, health monitoring systems, communication networks, online transactions in the financial market and so on. Data Streams are temporally ordered, fast changing, massive, and potentially infinite sequence of data. Data Stream mining is a very challenging problem. This is due to the fact that data streams are of tremendous volume and flows at very high speed which makes it impossible to store and scan streaming data multiple time. Concept evolution in streaming data further

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Chen, Jing, Peng Li, Weiqing Fang, et al. "Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data Stream." Wireless Communications and Mobile Computing 2021 (December 26, 2021): 1–17. http://dx.doi.org/10.1155/2021/6662254.

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Real-time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type-2 fuzzy set theory over data stream (WSWFFP-T2) with a single scan based on the artificial datasets of medical data stream. The weighted fuzzy frequent pattern tree based on type-2 fuzzy set theory (WFFPT2-tree) and fuzzy-list sorted structure (FLSS) is designed to mine the fuzzy frequent patterns (FF

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Xu, Kaikuo, Yexi Jiang, Mingjie Tang, Changan Yuan, and Changjie Tang. "PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/386180.

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Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we proposePRESEE(parameter-free, real-time, and scalable time-series stream segmentin

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Kokate, Umesh, Arvind Deshpande, Parikshit Mahalle, and Pramod Patil. "Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion." Big Data and Cognitive Computing 2, no. 4 (2018): 32. http://dx.doi.org/10.3390/bdcc2040032.

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Data growth in today’s world is exponential, many applications generate huge amount of data streams at very high speed such as smart grids, sensor networks, video surveillance, financial systems, medical science data, web click streams, network data, etc. In the case of traditional data mining, the data set is generally static in nature and available many times for processing and analysis. However, data stream mining has to satisfy constraints related to real-time response, bounded and limited memory, single-pass, and concept-drift detection. The main problem is identifying the hidden pattern

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Dhanaseelan, F. Ramesh, and M. JeyaSutha. "CIP- Efficient Method for Mining Frequent Itemsets from Data streams using Landmark Window Model." International Journal of Advanced Networking and Applications 14, no. 04 (2023): 5563–71. http://dx.doi.org/10.35444/ijana.2023.14409.

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Continuous stream transactions like network monitoring, retail market data analysis and stock market prediction need the “frequent patterns” to be detected recurrently. Literature suggests that several pattern mining solutions are being developed over years. Still lot of challenges need to be addressed due to rapidness in generation of continuous, unbounded and ordered data real time. Hence extraction of frequent patterns from recent data will improve the analysis of stream data. In this article, a new landmark window model CIP (candidate indexing and pruning) is considered for mining the data

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Wang, Ju, Fuxian Liu, and Chunjie Jin. "PHUIMUS: A Potential High Utility Itemsets Mining Algorithm Based on Stream Data with Uncertainty." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8576829.

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High utility itemsets (HUIs) mining has been a hot topic recently, which can be used to mine the profitable itemsets by considering both the quantity and profit factors. Up to now, researches on HUIs mining over uncertain datasets and data stream had been studied respectively. However, to the best of our knowledge, the issue of HUIs mining over uncertain data stream is seldom studied. In this paper, PHUIMUS (potential high utility itemsets mining over uncertain data stream) algorithm is proposed to mine potential high utility itemsets (PHUIs) that represent the itemsets with high utilities and

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Abid, Amal, Salma Jamoussi, and Abdelmajid Ben Hamadou. "AIS-Clus: A Bio-Inspired Method for Textual Data Stream Clustering." Vietnam Journal of Computer Science 06, no. 02 (2019): 223–56. http://dx.doi.org/10.1142/s2196888819500143.

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The spread of real-time applications has led to a huge amount of data shared between users. This vast volume of data rapidly evolving over time is referred to as data stream. Clustering and processing such data poses many challenges to the data mining community. Indeed, traditional data mining techniques become unfeasible to mine such a continuous flow of data where characteristics, features, and concepts are rapidly changing over time. This paper presents a novel method for data stream clustering. In this context, major challenges of data stream processing are addressed, namely, infinite leng

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Tekin, Cem, and Mihaela van der Schaar. "Actionable intelligence and online learning for semantic computing." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630011. http://dx.doi.org/10.1142/s2425038416300111.

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As the world becomes more connected and instrumented, high dimensional, heterogeneous and time-varying data streams are collected and need to be analyzed on the fly to extract the actionable intelligence from the data streams and make timely decisions based on this knowledge. This requires that appropriate classifiers are invoked to process the incoming streams and find the relevant knowledge. Thus, a key challenge becomes choosing online, at run-time, which classifier should be deployed to make the best possible predictions on the incoming streams. In this paper, we survey a class of methods

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Chen, Jiashun, Jianjing Chen, Zhaoman Zhong, Hao Zhang, and Mehmed Kantardzic. "LCTree-Based Approach for Mining Frequent Items in Real-Time." Computational Intelligence and Neuroscience 2022 (October 14, 2022): 1–17. http://dx.doi.org/10.1155/2022/7430106.

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With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree). We designed an algorithm by using an improved data structure to create objective tree, which can find frequent itemsets with linear complexity. Secondly, we can merge items in sliding windows by one scan with Head Linking List data structure. Third, by implementing data structure of Tail Linking Li

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Kenda, Klemen, and Dunja Mladenić. "Autonomous Sensor Data Cleaning in Stream Mining Setting." Business Systems Research Journal 9, no. 2 (2018): 69–79. http://dx.doi.org/10.2478/bsrj-2018-0020.

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Abstract Background: Internet of Things (IoT), earth observation and big scientific experiments are sources of extensive amounts of sensor big data today. We are faced with large amounts of data with low measurement costs. A standard approach in such cases is a stream mining approach, implying that we look at a particular measurement only once during the real-time processing. This requires the methods to be completely autonomous. In the past, very little attention was given to the most time-consuming part of the data mining process, i.e. data pre-processing. Objectives: In this paper we propos

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Grzenda, Maciej, Heitor Murilo Gomes, and Albert Bifet. "Delayed labelling evaluation for data streams." Data Mining and Knowledge Discovery 34, no. 5 (2019): 1237–66. http://dx.doi.org/10.1007/s10618-019-00654-y.

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AbstractA large portion of the stream mining studies on classification rely on the availability of true labels immediately after making predictions. This approach is well exemplified by the test-then-train evaluation, where predictions immediately precede true label arrival. However, in many real scenarios, labels arrive with non-negligible latency. This raises the question of how to evaluate classifiers trained in such circumstances. This question is of particular importance when stream mining models are expected to refine their predictions between acquiring instance data and receiving its tr

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Homayoun, Sajad, and Marzieh Ahmadzadeh. "A review on data stream classification approaches." Journal of Advanced Computer Science & Technology 5, no. 1 (2016): 8. http://dx.doi.org/10.14419/jacst.v5i1.5225.

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<p>Stream data is usually in vast volume, changing dynamically, possibly infinite, and containing multi-dimensional features. The attention towards data stream mining is increasing as regards to its presence in wide range of real-world applications, such as e-commerce, banking, sensor data and telecommunication records. Similar to data mining, data stream mining includes classification, clustering, frequent pattern mining etc. techniques; the special focus of this paper is on classification methods invented to handle data streams. Early methods of data stream classification needed all in

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Kompalli, Prasanna Lakshmi, and Ramesh Kumar Cherku. "Efficient Mining of Data Streams Using Associative Classification Approach." International Journal of Software Engineering and Knowledge Engineering 25, no. 03 (2015): 605–31. http://dx.doi.org/10.1142/s0218194015500059.

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Data stream associative classification poses many challenges to the data mining community. In this paper, we address four major challenges posed, namely, infinite length, extraction of knowledge with single scan, processing time, and accuracy. Since data streams are infinite in length, it is impractical to store and use all the historical data for training. Mining such streaming data for knowledge acquisition is a unique opportunity and even a tough task. A streaming algorithm must scan data once and extract knowledge. While mining data streams, processing time, and accuracy have become two im

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V Prasad, Gollanapalli, Kapil Sharma, Rama Krishna B, S. Krishna Mohan Rao, and Venkatadri M. "Labelled Classifier with Weighted Drift Trigger Model using Machine Learning for Streaming Data Analysis." International journal of electrical and computer engineering systems 13, no. 5 (2022): 349–56. http://dx.doi.org/10.32985/ijeces.13.5.3.

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The term “data-drift” refers to a difference between the data used to test and validate a model and the data used to deploy it in production. It is possible for data to drift for a variety of reasons. The track of time is an important consideration. Data mining procedures such as classification, clustering, and data stream mining are critical to information extraction and knowledge discovery because of the possibility for significant data type and dimensionality changes over time. The amount of research on mining and analyzing real-time streaming data has risen dramatically in the recent decad

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CHEN, HUI. "EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS." International Journal of Software Engineering and Knowledge Engineering 19, no. 05 (2009): 707–25. http://dx.doi.org/10.1142/s0218194009004325.

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Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data s

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Nagasuresh, Mr M., and Ms R. Roopa. "Big Data Stream Mining Using Integrated Framework with Classification and Clustering Methods." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2503–9. http://dx.doi.org/10.22214/ijraset.2023.50695.

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Abstract: The causes of numerous sorts of big data and data stream problems include the quick development of industry firms, the vast amount of data generated by these innovations, and the exponential growth of industrial company websites. There are numerous stream data mining algorithms for classification and grouping, each with its own unique set of attributes and important features. Ensemble classifiers aid in enhancing the greatest prediction performance results from these cutting-edge techniques. Ensemble approaches teach multiple types of classifiers and clusters rather than a single cla

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Huang, Fang, and Ningning Zheng. "A Novel Frequent Pattern Mining Algorithm for Real-time Radar Data Stream." Traitement du Signal 36, no. 1 (2019): 23–30. http://dx.doi.org/10.18280/ts.360103.

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Xu, Hua Fen, Jing Wu, and Guo Jun Mao. "The Key Technologies for Classification of Distributed Data Streams." Applied Mechanics and Materials 727-728 (January 2015): 976–81. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.976.

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With advances in data collection and generation technologies, environments that produce data streams is more and more. In recent years, the network application is further universal and the applications of a single data stream transfer toward a multi-node distributed data streams, such as sensor network, network monitoring, web log analysis and the credit card transaction data of multiple sites. These data is not only real-time, continuous and large scale, but also distributed. How to manage and analyze large dynamic datasets is an important subject that researchers are faced with. In view of t

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Fong, Simon, Yang Zhang, Jinan Fiaidhi, Osama Mohammed, and Sabah Mohammed. "Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy." BioMed Research International 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/274193.

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Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for the

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Hu, Shimin, Simon Fong, Lili Yang, et al. "Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm." Remote Sensing 13, no. 6 (2021): 1123. http://dx.doi.org/10.3390/rs13061123.

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Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it uns

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GABER, MOHAMED MEDHAT, and PHILIP S. YU. "DETECTION AND CLASSIFICATION OF CHANGES IN EVOLVING DATA STREAMS." International Journal of Information Technology & Decision Making 05, no. 04 (2006): 659–70. http://dx.doi.org/10.1142/s0219622006002179.

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Data stream mining has attracted considerable attention over the past few years owing to the significance of its applications. Streaming data is often evolving over time. Capturing changes could be used for detecting an event or a phenomenon in various applications. Weather conditions, economical changes, astronomical, and scientific phenomena are among a wide range of applications. Because of the high volume and speed of data streams, it is computationally hard to capture these changes from raw data in real-time. In this paper, we propose a novel algorithm that we term as STREAM-DETECT to cap

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Shafronenko, A. Yu, N. V. Kasatkina, Ye V. Bodyanskiy, and Ye O. Shafronenko. "CREDIBILISTIC ROBUST ONLINE FUZZY CLUSTERING IN DATA STREAM MINING TASKS." Radio Electronics, Computer Science, Control, no. 3 (October 13, 2023): 97. http://dx.doi.org/10.15588/1607-3274-2023-3-10.

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Context. The task of clustering-classification without a teacher of data arrays occupies an important place in the general problem of Data Mining, and for its solution there exists currently many approaches, methods and algorithms. There are quite a lot of situations where the real data to be clustered are corrupted with anomalous outliers or disturbances with non-Gaussian distributions. It is clear that “classical” methods of artificial intelligence (both batch and online) are ineffective in this situation. The goal of the paper is to develop a credibilistic robust online fuzzy clustering met

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Srivastava, Ritesh, and Veena Mittal. "ADAW: Age decay accuracy weighted ensemble method for drifting data stream mining." Intelligent Data Analysis 25, no. 5 (2021): 1131–52. http://dx.doi.org/10.3233/ida-205249.

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Dynamic environment data generators are very often in real-world that produce data streams. A data source of a dynamic environment generates data streams in which the underlying data distribution changes very frequently with respect to time and hence results in concept drifts. As compared to the stationary environment, learning in the dynamic environment is very difficult due to the presence of concept drifts. Learning in dynamic environment requires evolutionary and adaptive approaches to be accommodated with the learning algorithms. Ensemble methods are commonly used to build classifiers for

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Gajowniczek, Krzysztof, Marcin Bator, Tomasz Ząbkowski, Arkadiusz Orłowski, and Chu Kiong Loo. "Simulation Study on the Electricity Data Streams Time Series Clustering." Energies 13, no. 4 (2020): 924. http://dx.doi.org/10.3390/en13040924.

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Currently, thanks to the rapid development of wireless sensor networks and network traffic monitoring, the data stream is gradually becoming one of the most popular data generating processes. The data stream is different from traditional static data. Cluster analysis is an important technology for data mining, which is why many researchers pay attention to grouping streaming data. In the literature, there are many data stream clustering techniques, unfortunately, very few of them try to solve the problem of clustering data streams coming from multiple sources. In this article, we present an al

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Waiyamai, Kitsana, and Thanapat Kangkachit. "Constraint-based discriminative dimension selection for high-dimensional stream clustering." International Journal of Advances in Intelligent Informatics 4, no. 3 (2018): 167. http://dx.doi.org/10.26555/ijain.v4i3.271.

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Clustering data streams is one of active research topic in data mining. However, runtime of the existing stream clustering algorithms increases and their performance drop in the face of large number of dimensions. Complexity of the stream clustering methods is increased when perform on data with large number of dimensions. In order to reduce the clustering complexity, one possible solution consists in determining the appropriate subset of cluster dimensions via dimension projection. SED-Stream is an efficient clustering algorithm that supports high dimension data streams. The aim of this paper

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Su, Na, Shujuan Ji, and Jimin Liu. "Real-Time Topic Detection with Dynamic Windows." Computer Journal 63, no. 3 (2019): 469–78. http://dx.doi.org/10.1093/comjnl/bxz042.

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Abstract Microblog is a popular social network in which hot topics propagate online rapidly. Real-time topic detection can not only understand public opinion well but also bring high commercial value. We design a method for real-time microblog data analysis in order to detect popular long lasting events as well as emerging events. Firstly, a mining frequent items algorithm on microblog data stream is proposed to count approximate word frequency. This mining frequent items algorithm can find the frequent words for some time. Secondly, the windows size of the monitored words is adjusted dynamica

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GOMES, JOÃO BÁRTOLO, MOHAMED MEDHAT GABER, PEDRO A. C. SOUSA, and ERNESTINA MENASALVAS. "COLLABORATIVE DATA STREAM MINING IN UBIQUITOUS ENVIRONMENTS USING DYNAMIC CLASSIFIER SELECTION." International Journal of Information Technology & Decision Making 12, no. 06 (2013): 1287–308. http://dx.doi.org/10.1142/s0219622013500375.

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In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowl

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Fong, Simon, Justin Liang, Iztok Fister, Iztok Fister, and Sabah Mohammed. "Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm." Journal of Sensors 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/205707.

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Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately

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Chen, Pei Shuai, and Chong Huan Xu. "Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy." Applied Mechanics and Materials 26-28 (June 2010): 113–17. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.113.

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Mining maximal frequent itemsets get the advantage of a relatively small number of itemsets. Compared to mining frequent itemsets and mining frequent closed itemsets, such algorithm has higher time and space efficiency. According to the features of data streams and combined sliding window, a new algorithm E-FPMFI which is based on orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. The algorithm based on basic window updates information from data stream flow fragment and scans the stream only once to gain and store it in frequent itemsets list. The algorith

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Tang, Ke Ming, Cai Yan Dai, and Ling Chen. "ItemListFCI：An Algorithm for Mining Closed Frequent Itemsets Based on Bit Table." Applied Mechanics and Materials 44-47 (December 2010): 3159–63. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3159.

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Mining closed frequent itemsets in data streams is an important task in stream data mining. Most of the traditional algorithms for mining closed frequent itemsets are Apriori-based which find the frequent itemsets from large amount of candidates, and needs a great deal of time and space. In this paper, an algorithm ItemListFCI for mining closed frequent itemsets in data stream is proposed. The algorithm is based on the sliding window model, and uses a ItemList where the transactions and itemsets are recorded by the column and row vectors respectively. The algorithm first builds the ItemList fo

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Huo, Yan, Chengtao Yong та Yanfei Lu. "Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing". Wireless Communications and Mobile Computing 2018 (8 липня 2018): 1–13. http://dx.doi.org/10.1155/2018/6285719.

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In the Internet of Things (IoT), aggregation and release of real-time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenge

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Keskin, M. Erol, Dilek Taylan, and Ecir Ugur Kucuksille. "Data mining process for modeling hydrological time series." Hydrology Research 44, no. 1 (2012): 78–88. http://dx.doi.org/10.2166/nh.2012.003.

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The main purpose of this study was to develop an optimum flow prediction model, based on data mining process. The data mining process was applied to predict river flow of Seyhan Stream in the southern part of Turkey. Hydrological time series modeling was applied using monthly historical flow records to predict Seyhan Stream flows. Seyhan Stream flows were modeled by Markov models and it was seen that it adapted AR(2). Hence, Ft–2 and Ft–1 flows in (t–2) and (t–1) months were the taken inputs. For monthly streamflow predictions, data were taken from the General Directorate of Electrical Power R

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Hashemi, Sattar, Mohammadreza Kangavari, and Ying Yang. "Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams." Fundamenta Informaticae 88, no. 1-2 (2008): 135–60. https://doi.org/10.3233/fun-2008-881-206.

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In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised accordingly and timely. To detect concept change, a common method is to observe the online classification accuracy. If accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this methodology is that any drop in accuracy can be interpreted as a symptom of concept change. Unfortuna

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Kazanskiy, Nikolay, Vladimir Protsenko, and Pavel Serafimovich. "Performance analysis of real-time face detection system based on stream data mining frameworks." Procedia Engineering 201 (2017): 806–16. http://dx.doi.org/10.1016/j.proeng.2017.09.602.

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Keshvani, Twinkle, Madhu Shukla, Meghnesh Jayswal, and Kishan Makadiya. "Improving Performance Parameters of Clusters Using Density-Based Algorithm." Applied and Computational Engineering 2, no. 1 (2023): 100–110. http://dx.doi.org/10.54254/2755-2721/2/20220605.

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With the advancement in technology, data generated by non-stationary in day-to-day life is massive, continuous and rapid. Many applications such as IoT, transaction systems, network sensors, video surveillance systems, and network intrusion detection systems generate a massive amount of real-time data. The data used in traditional data mining is static in nature, and it can be revised for processing and Analysis. While data in data stream mining is dynamic in nature and it never stops. Besides, the data generated may have a change imbibed in its characteristics over a long/short period of time

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Loo, H. R., S. B. Joseph, and M. N. Marsono. "Online Incremental Learning for High Bandwidth Network Traffic Classification." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1465810.

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Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such as Field Programmable Gate Array (FPGA). This paper proposes an algorithm for online network traffic classification based on the concept of incrementalk-means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incrementalk-means (Euclidean and

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Kuthadi, Venu Madhav, and Rajalakshmi Selvaraj. "An Efficient Closed Frequent Item Sets Mining Algorithm-For Mining Closed Frequent Item Sets from Data Streams." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 7467–74. http://dx.doi.org/10.1166/jctn.2016.5741.

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A data stream is a continuous sequence of data elements generated from a specified source. Mining frequent item sets in dynamic databases and data streams encounters some challenges that make the mining task harder than static databases. Many research works were developed in the frequent itemset mining, but these methods have the familiar problem of memory usage and processing time. Because, in data streams data elements are arrive at a rapid rate. The incoming data is unbounded and probably infinite. Due to high speed and large amount of incoming data, frequent item set mining algorithm must

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