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A buffer-based online clustering for evolving data stream

INFORMATION SCIENCES 2019年 489卷 113-135页

作者： Islam, Md. Kamrul Ahmed, Md. Manjur Zamli, Kamal Z. Univ Malaysia Pahang Fac Comp Syst & Software Engn Kuantan 26300 Pahang Malaysia Univ Barisal Dept Comp Sci & Engn Kornokathi 8200 Barisal Bangladesh

data stream clustering plays an important role in data stream mining for knowledge extraction. Numerous researchers have recently studied density-based clustering algorithms due to their capability to generate arbitrarily shaped clusters. However, most of the algorithms are either fully offline, hybrid online/offline, or cannot handle the property of evolving data stream. Recently, a fully online clustering algorithm for evolving data stream called CEDAS was proposed. However, similar to other density-based clustering algorithms, CEDAS requires predefining the global optimal radius of micro-clusters, which is a difficult task: in addition, an erroneous choice deteriorates cluster performance. Moreover, the algorithm ignores the presence of temporarily irrelevant micro-clusters, which may be relevant in the future. In this study, we present a fully online density-based clustering algorithm called buffer-based online clustering for evolving data stream (BOCEDS). This algorithm recursively updates the micro-cluster radius to its local optimal. It also introduces a buffer for storing irrelevant micro-clusters and a fully online pruning method for extracting the temporarily irrelevant micro-cluster from the buffer. In addition, BOCEDS proposes an online micro-cluster energy-updating function based on the spatial information of the data stream. Experimental results are compared with those of CEDAS and other alternative hybrid online/offline density-based clustering algorithms, and BOCEDS proves its superiority over the other clustering algorithms. The sensitivity of clustering parameters is also measured. The proposed algorithm is then applied to real-world weather data streams to demonstrate its capability to detect changes in data stream and discover arbitrarily shaped clusters. The proposed BOCEDS can be available in https://***/view/md-manjur-ahmed and https://***/view/kamrul-just. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Density-based clustering evolving data stream Arbitrarily shaped cluster Clustering graph

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Hyper-ellipsoidal clustering technique for evolving data stream

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KNOWLEDGE-BASED SYSTEMS 2014年 70卷 3-14页

作者： Rehman, Muhammad Zia-ur Li, Tianrui Yang, Yan Wang, Hongjun Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China

data mining has become a key ingredient in establishing intelligent decision support systems. As one of main branches in data mining, data stream clustering has received much attention over the past decade. Most existing data stream clustering techniques count on Euclidean distance metric for finding similar objects and hence produce spherical clusters which are not always suitable to represent the data. Moreover, in most of the real world problems, we come across the data of varying density which cannot be handled by density-based clustering techniques. In this paper, we introduce a new clustering technique called Hyper-Ellipsoidal Clustering for evolving data stream (HECES) based on the recently proposed HyCARCE algorithm. In HECES, a few modifications in the HyCARCE algorithm are made for handling stream clustering problem: sliding window model is used to handle incoming stream of data to minimize the impact of the obsolete information on recent clustering results;shrinkage technique is used to avoid the singularity issue in finding the covariance of correlated data;a novel technique for merging the initial ellipsoids is used to obtain the final clusters instead of a computationally intensive process of expansion and adjustment. HECES relies on Mahalanobis distance metric to cluster the data points and hence results in ellipsoidal shaped clusters. It can successfully handle data of varying density. Experiments on various synthetic and real datasets for clustering streaming data provide a comparative validation of our approach. (C) 2013 Elsevier B.V. All rights reserved.

关键词： data mining Decision support systems Hyper-ellipsoidal clustering evolving data stream data clustering

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MapReduce based Frameworks for Classifying evolving data stream

MapReduce based Frameworks for Classifying Evolving Data Str...

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IEEE 13th International Conference on data Mining (ICDM)

作者： Haque, Ahsanul Khan, Latifur Univ Texas Dallas Dept Comp Sci Richardson TX 75083 USA

ISBN: (纸本)9780769551098

data stream mining has some inherent challenges which are not present in traditional data mining. stream data classification is a challenging problem because of two important properties: its infinite length and evolving nature. In our current work, we have proposed a multi-tiered ensemble based method to address these challenges of data stream mining. Though, it is a fast and robust method, it needs to build large number of Ada Boost ensembles for each numeric feature after receiving each new data chunk. Thus, it can face scalability issue especially when size of the data chunk is large or the number of numeric attributes is high. To address this problem, we propose two different approaches to form these large number of Ada Boost ensembles with Map Reduce based parallelism. Each of these approaches help our base method to achieve significant scalability without compromising classification accuracy. We compare these approaches from different aspects of design. We also demonstrate and compare performance of the approaches on benchmark datasets in terms of execution time, speedup and scaleup.

关键词： evolving data stream Scalability MapReduce Distributed Processing

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An autoencoder-based fast online clustering algorithm for evolving data stream 23

An autoencoder-based fast online clustering algorithm for ev...

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2nd Asia Conference on Algorithms, Computing and Machine Learning (CACML)

作者： Gao, Dazheng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781450399449

In the era of Big data, more and more IoT devices are generating huge amounts of high-dimensional, real-time and dynamic data streams. As a result, there is a growing interest in how to cluster this data effectively and efficiently. Although a number of popular two-stage data stream clustering algorithms have been proposed, these algorithms still have some problems that are difficult to solve in the face of real-world data streams: poor handling of high-dimensional data streams and difficulty in effective dimensionality reduction;a slow clustering process that makes it difficult to meet real-time requirements;and too many manually defined parameters that make it difficult to cope with evolving data streams. This paper proposes an autoencoder-based fast online clustering algorithm for evolving data stream(AFOCEDS). The algorithm uses a stacked denoising autoencoder to reduce the dimensionality of the data, a multi-threaded approach to improve response speed, and a mechanism to automatically update parameters to cope with evolving data streams. The experiments on several realistic data streams show that AFOCEDS outperforms other algorithms in terms of effectiveness and speed.

关键词： evolving data stream cluster autoencoder

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Concept Drift-Based Intrusion Detection For evolving data stream Classification In IDS: Approaches And Comparative Study

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COMPUTER JOURNAL 2024年第7期67卷 2529-2547页

作者： Seth, Sugandh Chahal, Kuljit Kaur Singh, Gurvinder Guru Nanak Dev Univ Dept Comp Sci & Engn Grand Trunk RdOff NH 1 Amritsar 143005 Punjab India

Static machine and deep learning algorithms are commonly used in intrusion detection systems (IDSs). However, their effectiveness is constrained by the evolving data distribution and the obsolescence of the static data sources used for model training. Consequently, static classifiers lose efficacy, necessitating expensive model retraining with time. The aim is to develop a dynamic and adaptable IDS that mitigates the limitations of static models, ensuring real-time threat detection and reducing the need for frequent, resource-intensive model retraining. This research proposes an approach that amalgamates the adaptive random forest (ARF) classifier with Hoeffding's bounds and a moving average test for the early and accurate detection of network intrusions. The ARF can adapt in real time to shifting network conditions and evolving attack patterns, constantly refining its intrusion detection capabilities. Furthermore, the inclusion of Hoeffding's bounds and the moving average test adds a dimension of statistical rigor to the system, facilitating the timely recognition of concept drift and distinguishing benign network variations from potential intrusions. The synergy of these techniques results in reduced false positives and false negatives, thereby enhancing the overall detection rate. The proposed method delivers outstanding results, with 99.95% accuracy and an impressive 99.96% recall rate on the latest CIC-IDS 2018 dataset, outperforming the results of existing approaches.

关键词： intrusion detection system concept drift stream-oriented learning machine learning deep learning adaptive algorithms evolving data stream model adaptation dynamic environments online learning

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Incremental Clustering Approach for evolving Trajectory data stream 6

Incremental Clustering Approach for Evolving Trajectory Data...

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6th International Electrical Engineering Congress (iEECON)

作者： Shein, Thi Thi Puntheeranurak, Sutheera King Mongkuts Inst Technol Ladkrabang Dept Comp Engn Fac Engn Bangkok Thailand

ISBN: (纸本)9781538623176

Trajectory data stream contain an enormous amount of data about spatial and temporal information of moving objects. Clustering the trajectory may bring benefits to several applications such as traffic monitoring system, behavior analysis of animal movement pattern, weather forecasting. In many real applications, trajectory data keep coming into the database or server for immediate analysis. Most existing approaches analyze the whole object trajectory from the static database rather than the current movement dynamic data. These methods cannot get a concise result because the objects always move then their position has changed over time. In this paper, we address the problem of monitoring the evolution of moving objects over time and propose incremental Sub-Trajectory Clustering based on Micro-group (iSTCM) framework to reduce computational time complexity. As an experiment, the performance of our proposed algorithm will conduct on real taxicab datasets and compare the efficiency and cluster quality as effectiveness with another state of the art methods.

关键词： spatial-temporal data mining evolving data stream sub-trajectory clustering

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Efficient Discovery of Traveling Companion from evolving Trajectory data stream 42

Efficient Discovery of Traveling Companion from Evolving Tra...

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42nd Annual IEEE-Computer-Society Computers, Software and Applications (COMPSAC) Conference - Staying Smarter in a Smartening World

作者： Thi Thi Shein Puntheeranurak, Sutheera Imamura, Makoto King Mongkuts Inst Technol Ladkrabang Fac Engn Dept Comp Engn Bangkok Thailand Tokai Univ Dept Embedded Technol Tokyo Japan

ISBN: (纸本)9781538626672

Trajectory data stream contain an enormous amount of data about spatial and temporal information of moving objects. Discovering useful pattern from moving objects can convey valuable knowledge to a variety applications such as transportation management, military surveillance, and weather forecasting by analyzing animal movement behaviors. In many real applications, trajectory data keep coming into the database or server for immediate analysis. Most moving objects' pattern discovery approaches analyze the data by re-computing from scratch. Now, existing some group pattern approaches incrementally illuminate this problem. However, there was still high computational time complexity to the efficient and accurate discovery of traveling together moving objects(i.e., traveling companion) from evolving data streams. In this paper, we propose micro-group based clustering algorithm over evolving data stream to reduce computational time complexity. Experiments of this proposed system will conduct on real taxi trajectory data and synthetic data.

关键词： spatiotemporal pattern mining evolving data stream clustering

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GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for evolving data streams

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM data 2018年第2期12卷 1–33页

作者： Bonab, Hamed R. Can, Fazli Bilkent Univ Ankara Turkey Bilkent Univ Bilkent Informat Retrieval Grp Comp Engn Dept TR-06800 Ankara Turkey

Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is a well-known solution. It is possible that a subset of classifiers in the ensemble outperforms others in a time-varying fashion. However, optimum weight assignment for component classifiers is a problem, which is not yet fully addressed in online evolving environments. We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances. We map vote scores of individual classifiers and true class labels into a spatial environment. Based on the Euclidean distance between vote scores and ideal-points, and using the linear least squares (LSQ) solution, we present a novel, dynamic, and online weighting approach. While LSQ is used for batch mode ensemble classifiers, it is the first time that we adapt and use it for online environments by providing a spatial modeling of online ensembles. In order to show the robustness of the proposed algorithm, we use real-world datasets and synthetic data generators using the Massive Online Analysis (MOA) libraries. First, we analyze the impact of our weighting system on prediction accuracy through two scenarios. Second, we compare GOOWE with eight state-of-theart ensemble classifiers in a comprehensive experimental environment. Our experiments show that GOOWE provides improved reactions to different types of concept drift compared to our baselines. The statistical tests indicate a significant improvement in accuracy, with conservative time and memory requirements.

关键词： Ensemble classifier concept drift evolving data stream dynamic weighting geometry of voting least squares spatial modeling for online ensembles

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VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

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data MINING AND KNOWLEDGE DISCOVERY 2021年第6期35卷 2679-2713页

作者： Bernardo, Alessio Della Valle, Emanuele Politecn Milan DEIB Milan Italy

The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the very fast continuous synthetic minority oversampling technique (VFC-SMOTE). It is a novel meta-strategy to be prepended to any streaming machine learning classification algorithm aiming at oversampling the minority class using a new version of Smote and Borderline-Smote inspired by data Sketching. We benchmarked VFC-SMOTE pipelines on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that VFC-SMOTE pipelines learn models whose minority class performances are better than state-of-the-art. Moreover, we analyze the time/memory consumption and the concept drift recovery speed.

关键词： SML evolving data stream Concept drift Balancing data sketching

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Discussion and review on evolving data streams and concept drift adapting

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evolving SYSTEMS 2018年第1期9卷 1-23页

作者： Khamassi, Imen Sayed-Mouchaweh, Moamar Hammami, Moez Ghedira, Khaled Univ Tunis Inst Super Gest Tunis SOIE 41 Rue Liberte Le Bardo 2000 Tunisia Mines Douai IA F-59500 Douai France

Recent advances in computational intelligent systems have focused on addressing complex problems related to the dynamicity of the environments. In increasing number of real world applications, data are presented as streams that may evolve over time and this is known by concept drift. Handling concept drift is becoming an attractive topic of research that concerns multidisciplinary domains such that machine learning, data mining, ubiquitous knowledge discovery, statistic decision theory, etc... Therefore, a rich body of the literature has been devoted to the study of methods and techniques for handling drifting data. However, this literature is fairly dispersed and it does not define guidelines for choosing an appropriate approach for a given application. Hence, the main objective of this survey is to present an ease understanding of the concept drift issues and related works, in order to help researchers from different disciplines to consider concept drift handling in their applications. This survey covers different facets of existing approaches, evokes discussion and helps readers to underline the sharp criteria that allow them to properly design their own approach. For this purpose, a new categorization of the existing state-of-the-art is presented with criticisms, future tendencies and not-yet-addressed challenges.

关键词： Adaptive learning evolving learning evolving data stream Change detection Concept drift Statistical hypothesis test

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