A Clustering Algorithm Based on Density-Grid for Stream Data

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.

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.

Data stream clustering has emerged as a challenging and interesting problem over the past few years. Due to the evolving nature, and one-pass restriction imposed by the data stream model, traditional clustering algorithms are inapplicable for stream clustering. This problem becomes even more challenging when the data is high-dimensional and the clusters are not linearly separable in the input space. In this paper, we propose a nonlinear stream clustering algorithm that adapts to the stream's evolutionary changes. Using the kernel methods for dealing with the non-linearity of data separation, we propose a novel 2-tier stream clustering architecture. Tier-1 captures the temporal locality in the stream, by partitioning it into segments, using a kernel-based novelty detection approach. Tier-2 exploits this segment structure to continuously project the streaming data nonlinearly onto a low-dimensional space (LDS), before assigning them to a cluster. We demonstrate the effectiveness of our approach through extensive experimental evaluation on various real-world datasets.

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.

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

© 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 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

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 algorithms are important methods widely used in mining data streams because of their abilities to deal with infinite data flows. Although these algorithms perform well to mining latent relationship in data streams, most of them suffer from loss of cluster purity and become unstable when the inputting data streams have too many noisy variables. In this article, we propose a clustering algorithm to cluster data streams with noisy variables. The result from simulation shows that our proposal method is better than previous studies by adding a process of variable selection as a component in clustering algorithms. The results of two experiments indicate that clustering data streams with the process of variable selection are more stable and have better purity than those without such process. Another experiment testing KDD-CUP99 dataset also shows that our algorithm can generate more stable result.

In order to solve the contradiction between the accuracy and the realtime in radar signal sorting,a dynamic signal sorting frame based on data stream clustering is proposed.The frame can be divided into two parts,or online part and offline part.In online part,the reconnaissance data is transformed and saved as grid frame.In offline part,the grid frame is sorted with the algorithm of grid-based clustering.Experimental results show that the proposed frame can sort the high density and complex reconnaissance data stream without any prior knowledge,and is not sensitive to the influence of noise.The requirement of accuracy and real-time in signal sorting can be satisfied with this frame.

The main attention of research on data stream clustering algorithms so far has been focused on the adaptation of the algorithms for static datasets to the data streams and improvements of the existing adapted algorithms. Such algorithms fulfil the purpose of the first-order learning from data to clusters. This paper prompts a new question on second-order learning of cluster models from data streams and presents a learning algorithm that detects persistent clusters from consecutive clustering snapshots in data streams. In this work, we first collect a sequence of cluster snapshots as the output clusters at selected query points and then identify the persistent clusters within a given timeframe. The algorithm is evaluated on collections of synthetic datasets. The experimental results have demonstrated the effectiveness of the algorithm in detecting such persistent clusters.

Streaming data is widely generated in our lives. This has promoted a lot of research on streaming data mining, such as streaming data clustering and filtering. In our work, we present a problem about data stream processing, namely, streaming data sorting. There are some important characteristics of streaming data. Firstly, streaming data comes in the form of streams. It is usually assumed that streaming data is infinite, so it cannot be stored completely in memory. Secondly, we must process the streaming data in real time, otherwise we may lose the opportunity to deal with it forever. Based on these characteristics, we propose a dynamic algorithm that can make full use of memory and minimize error to solve the problem of streaming data sorting, which is further combined with the BFR algorithm to sort a particular type of streaming data. Some experiments are conducted to confirm the effectiveness of the proposed algorithms.

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.

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.