Clustering Parallel Data Streams

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.

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.

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.

© 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.

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.

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

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.

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.

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.

Clustering data streams has become a hot topic in the era of big data. Driven by the ever increasing volume, velocity and variety of data, more efficient algorithms for clustering large-scale complex data streams are needed. In this paper, we present a parallel algorithm called PaStream, which is based on advanced Graphics Processing Unit (GPU) and follows the online-offline framework of CluStream. Our approach can achieve hundreds of times speedup on high-speed and high-dimensional data streams compared with CluStream. It can also discover clusters with arbitrary shapes and handle outliers properly. The efficiency and scalability of PaStream are demonstrated through comprehensive experiments on synthetic and standard benchmark datasets with various problem factors.

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.

This paper introduces a strategy for clustering online multiple data streams. We assume that several sources are used for recording, over time, data about some physical phenomena. Each source provides repeated measurements at a very high frequency so that it is not possible to store the whole amount of data into some easy-to-access media, but data are available only in batches. Our aim is to discover a partition of the sources (e.g. sensors) into homogeneous clusters, analysing the incoming streams of data. The proposed strategy is based on processing the incoming data batches independently, through an initial summarization of the data batches by histograms and, then, by means of a local clustering performed on the histograms which provides a further data summarization. To keep track of the data proximities among the data streams over time, we use local clustering outputs for updating a proximity matrix. The final partitioning of the streams is obtained by a clustering based on such proximity matrix. Through an application on real and simulated data, we show the effectiveness of our strategy in finding homogeneous groups of sources of data streams.

Genetic Programming is an evolutionary soft computing approach. Data streams are the order of the day input sources. In general, data streams exhibit a peculiar behavior of drifting the concepts as time passes by. Here is a study of GP Classifier on Concept Drifting Data Streams. GP classifier performance is compared to that of other state-of-the-art data mining and stream classification approaches. Boosting is a machine learning meta-algorithm for performing supervised learning. A weak learner is defined to be a classifier which is only slightly correlated with the true classification. In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification. Boosting combines a set of weak learners to create a strong learner. It is observed that the Boosting GP approach is beating Boosting Naive Bayes classification on Concept Drifting Data Streams. Hence it is found that GP is a competent algorithm for Concept Drifting Data Stream classification.

Mining maximal frequent patterns by considering weight conditions over data streams

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.