Adaptive non-linear clustering in data streams

This article studies a new problem of data stream clustering, namely, multiview data stream (MVStream) clustering. Although many data stream clustering algorithms have been developed, they are restricted to the single-view streaming data, and clustering MVStreams still remains largely unsolved. In addition to the many issues encountered by the conventional single-view data stream clustering, such as capturing cluster evolution and discovering clusters of arbitrary shapes under the limited computational resources, the main challenge of MVStream clustering lies in integrating information from multiple views in a streaming manner and abstracting summary statistics from the integrated features simultaneously. In this article, we propose a novel MVStream clustering algorithm for the first time. The main idea is to design a multiview support vector domain description (MVSVDD) model, by which the information from multiple insufficient views can be integrated, and the outputting support vectors (SVs) are utilized to abstract the summary statistics of the historical multiview data objects. Based on the MVSVDD model, a new multiview cluster labeling method is designed, whereby clusters of arbitrary shapes can be discovered for each view. By tracking the cluster labels of SVs in each view, the cluster evolution associated with concept drift can be captured. Since the SVs occupy only a small portion of data objects, the proposed MVStream algorithm is quite efficient with the limited computational resources. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method.

Recently data stream has been extensively explored due to its emergence in large deal of applications such as sensor networks,web click streams and network flows. Vast majority of researches in the context of data stream mining are devoted to superviselearning, whereas, in real word human practice label of data are rarely available to the learning algorithms. Hence, clustering asthe most important unsupervised learning has been in the gravity of focus of quite a lot number of the researchers in data streamcommunity. Clustering paradigms basically place the similar objects together and separate the dissimilar ones into differentclusters.In this paper, we propose a Statistical framework for data Stream Clustering, which abbreviated as StatisStreamClust that makesuse of two components to find clusters in data stream. The first component especially designed to detect concept change wheredata underlying distributions change from time to time. Upon detection of concept change by the first component, the secondcomponent is triggered to update the whole clustering model. StatisStreamClust brings great benefits to data stream clusteringincluding no sensitivity to the number of clusters and dimensions, reasonable complexity and in the meantime desirable performance,and finally no need to determine window size a priori. To explore the advantages of our approach, quite a lot ofexperiments with different settings and specifications are conducted. The obtained results are very promising.

Many real applications, such as network traffic monitoring, intrusion detection, satellite remote sensing, and electronic business, generate data in the form of a stream arriving continuously at high speed. Clustering is an important data analysis tool for knowledge discovery. Compared with traditional clustering algorithms, clustering stream data is an important and challenging problem which has attracted many researchers. Clustering stream data is facing two main challenges. First, as the data is continuously arriving with high rate and the computer storage capacity is limited, raw data can only be scaned in one pass. Second, stream data is always changing with time, so viewing a data stream as a set of static data can deteriorate the clustering quality. In fact, users are more concerned with the evolving behaviors of clusters which can help people making correct decisions. This paper proposes a density-grid based clustering algorithm, PKS-Stream-I, for stream data. It is an optimization of PKS-Stream in density detection period selection, sporadic grid detection and removal. Empirical results show the proposed method yields out better performance.

© 2019 Association for Computing Machinery.Number of connected devices is steadily increasing and these devices continuously generate data streams. These data streams are often high dimensional and contain concept drift. Real-time processing of data streams is arousing interest despite many challenges. Clustering is a method that does not need labeled instances (it is unsupervised) and it can be applied with less prior information about the data. These properties make clustering one of the most suitable methods for real-time data stream processing. Moreover, data embedding is a process that may simplify clustering and makes visualization of high dimensional data possible. There exist several data stream clustering algorithms in the literature, however no data stream embedding method exists. UMAP is a data embedding algorithm that is suitable to be applied on data streams, but it cannot adopt concept drift. In this study, we have developed a new method to apply UMAP on data streams, adopt concept drift and cluster embedded data instances using any distance based clustering algorithms.

Clustering data streams has become a hot topic and has been extensively applied to many real-world applications. Compared with traditional clustering, data stream clustering is more challenging. Adaptive Resonance Theory (ART) is a powerful (online) clustering method, it can automatically adjust to learn both abstract and concrete information, and can respond to arbitrarily large non-stationary databases while having fewer parameters, low computational complexity, and less sensitivity to noise, but its limited feature representation hinders its application to complex data streams. In this paper, considering its advantages and disadvantages, we present its flexible extension for stream clustering, called fractional adaptive resonance theory (FRA-ART). FRA-ART enhances data representation by fractionally exponentiating input features using self-interactive basis functions (SIBFs) and incorporating feature interaction through cross-interactive basis functions (CIBFs) at the cost only of introducing an additionally adjustable fractional order. Both SIBFs and CIBFs can be precomputed using existing algorithms, making FRA-ART easily adaptable to any ART variant. Finally, comparative experiments on five data stream datasets, including artificial and real-world datasets, demonstrate FRA-ART’s superior robustness and comparable or improved performance in terms of accuracy, normalized mutual information, rand index, and cluster stability compared to ART and the state-of-the-art G-Stream algorithm.

PTS: Projected Topological Stream clustering algorithm

Data stream mining is a research area that has grown enormously in recent years. The main challenge is to extract knowledge in real-time from a possibly unbounded stream of data. Clustering, a process in which groupings within the data are to be identified, data streams is an useful technique to extract and identify underlying structures of the data. An open question in the field of stream clustering is how to evaluate the proposed algorithms. In this survey, we review the literature in the domain to identify the common methodologies, datasets, and evaluation measures, used to evaluate the algorithms. We provide a short summary of the stream clustering algorithms in the literature, but our primary focus lies in the survey of cluster validation relevant to the evaluation of data stream clustering algorithms. We begin our literature review with the inception of clustering incrementally, namely with the introduction of the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm.We identify that the evaluation methodologies primarily focus on performance, and that aspects such as cluster quality are rarely visited. Performance has been the focal point of all evaluation, both in terms of computational performance and accuracy, since the inception of clustering data streams. We also identify that issues that exist in the conventional clustering domain are also present in the data stream clustering. However, minor additions to the evaluation methods can improve both the applicability and usefulness of the algorithms.

The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

Massive volumes of data streams can be found in numerous applications such as network intrusion detection, financial transaction flows, telephone call records, sensor streams, and meteorological data. In recent years, there are increasing demands for mining data streams. Unlike the finite, statically stored data sets, stream data are massive, continuous, temporally ordered, dynamically changing, and potentially infinite [5]. For example, Cortes et al. report that AT&T long distance call records consist of 300 million records per day for 100 million customers. For the stream data applications, the volume of data is usually too huge to be stored or to be scanned for more than once. Further, in data streams, the data points can only be sequentially accessed. Random access to data is not allowed. Extensive research has been done for mining data streams, including those on the stream data classification [3, 20], mining frequent patterns [9, 17, 18], and clustering stream data [1, 2, 8, 9, 10, 11, 12, 13, 14, 16, 19]. In this paper, we study the clustering of multiple and parallel data streams. Our study should be differentiated from some previous studies on clustering stream data [19, 1]. Our goal is to group multiple streams with similar behavior and trend together, instead of to cluster the data records within one data stream. There are various applications where it is desirable to cluster the streams themselves rather than the individual data records within them. For example, the price of a stock may rise and fall from time to time. To reduce the financial risk, an investor may prefer to spread his investment over a number of stocks which may exhibit different behaviors. As another application, in meteorological study and disaster prediction, it is useful to cluster meteorological data streams from different geographical regions of similar curvature trends in order to identify regions with similar meteorological behaviors. Yet another example is that a super market may record sales on different merchandizes. There may be some relationship among the sales of different merchandizes and thus the merchant can make use of the correlation to manipulate the prices to maximize the profit. Clustering refers to partition a data set into clusters such that members within the same cluster are similar in a certain sense and members of different clusters are dissimilar. Current clustering techniques can be broadly classified into several categories: partitioning methods (e.g., k-means and k-medoids), hierarchical methods (e.g. BIRCH [22]), densitybased methods (e.g. DBSCAN [15]), and grid-based methods (e.g. CLIQUE [4]). However, these methods are designed only for static data sets and can not be directly applied to data streams. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

In the case of real-world data streams, the underlying data distribution will not be static; it is subject to variation over time, which is known as the primary reason for concept drift. Concept drift poses severe problems to the accuracy of a model in online learning scenarios. The recurring concept is a particular case of concept drift where the concepts already seen in the past reappear as the stream evolves. This problem is not yet studied in the context of stream clustering. This paper proposes a novel algorithm for identifying the recurring concepts in data stream clustering. During concept recurrence, the most matching model is retrieved from the repository and reused. The algorithm has minimum memory requirements and works online with the stream. Some of the concepts and definitions, already familiar in concept recurrence studies of stream classification have been redefined for clustering. The experiments conducted on real and synthetic data streams reveal that the proposed algorithm has the potential to identify recurring concepts.

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the number of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density-based methods in the clustering process and at the same time overcoming the constraints, which are put out by data stream’s nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms’ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.

Based on the traditional grid density clustering algorithm, proposing A Data Stream Clustering Algorithm Based on Density and Extended Grid(DEGDS). The algorithm combines the advantages of grid clustering algorithm and density clustering algorithm, by improving the defects of clustering parameters by artificially set, get any shape of the cluster. The algorithm uses the local density of each sample point and the distance from the other sample points, determining the number of clustering centers in the grid, and realizing the automatic determination of the clustering center, which avoids the influence of improper selection of initial centroid on clustering results. And in the process of combining the Spark parallel framework for partitioning the data to achieve its parallelization. For data points clustered outside the grid, the clustering within the grid has been effectively expanded by extending the grid, to ensure the accuracy of clustering. Introduced density estimation is connected and grid boundaries to merging grid, saving memory consumption. Using the attenuation factor to incremental update grid density, reflect the evolution of spatial data stream. The experimental results show that compared with the traditional clustering algorithm, the DEGDS algorithm has a large performance improvement in accuracy and efficiency, and can be effectively for large data clustering.

针对数据流的聚类算法，近年来取得了有效的进展，出现了许多卓有成效的算法。随着信息采集技术的进步，需要处理的数据量越来越大，需要研究针对数据流的并行聚类算法。本文基于串行的数据流聚类算法D-Stream作出并行化改进，用通用的大数据处理框架Spark设计了一个基于分布式架构运行的动态数据聚类算法PDStream。实验结果表明，该算法具有更高的效率和良好的扩展性，能够实现分布式架构下的流数据动态聚类。 In the era of big data, with the rapid growth of data size, the requirements of data processing in-crease constantly. It has put forward many effective algorithms for data stream clustering these years. However, with the continuous development of social technology, single machine environ-ment has been difficult to meet the needs of data mining. Cluster environment is used more for information collection and data processing, the traditional clustering algorithm does not adapt well to the new processing requirements. This paper made some improvements from the data stream clustering algorithm D-Stream, used the big data processing framework Spark and designed a dynamic data clustering algorithm PDStream based on distributed architecture. The new algorithm is proved to be more efficient and able to perform dynamic clustering tasks under distributed architecture from the results of experiment.

Clustering data stream is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. Several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with dynamic data that arrive in an online fashion, capable of performing fast and incremental processing of data objects, and suitably addressing time and memory limitations. In this paper, we propose a semi-supervised clustering algorithm that extends Affinity Propagation (AP) to handle evolving data steam. We incorporate a set of labeled data items with set of exemplars to detect a change in the generative process underlying the data stream, which requires the stream model to be updated as soon as possible. Experimental results with state-of-the-art data stream clustering methods demonstrate the effectiveness and efficiency of the proposed method.KeywordsAffinity propagationdata streamssemi-supervised clustering

Stream clustering has emerged as a vital area for processing streaming data in real-time, facilitating the extraction of meaningful information. While efficient approaches for defining and updating clusters based on similarity criteria have been proposed, outliers and noisy data within stream clustering areas pose a significant threat to the overall performance of clustering algorithms. Moreover, the limitation of existing methods in generating non-spherical clusters underscores the need for improved clustering quality. As a new methodology, we propose a new stream clustering approach, MCMSTStream, to overcome the abovementioned challenges. The algorithm applies MST to micro-clusters defined by using the KD-Tree data structure to define macro-clusters. MCMSTStream is robust against outliers and noisy data and has the ability to define clusters with arbitrary shapes. Furthermore, the proposed algorithm exhibits notable speed and can handling high-dimensional data. ARI and Purity indices are used to prove the clustering success of the MCMSTStream. The evaluation results reveal the superior performance of MCMSTStream compared to state-of-the-art stream clustering algorithms such as DenStream, DBSTREAM, and KD-AR Stream. The proposed method obtained a Purity value of 0.9780 and an ARI value of 0.7509, the highest scores for the KDD dataset. In the other 11 datasets, it obtained much higher results than its competitors. As a result, the proposed method is an effective stream clustering algorithm on datasets with outliers, high-dimensional, and arbitrary-shaped clusters. In addition, its runtime performance is also quite reasonable.