Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams.

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.

Stream clustering is a standout amongst the most imperative fields in machine learning. Traditional unsupervised clustering tasks have been normally carried out in batch mode where data could be somehow fitted in memory and therefore several passes on the data are allowed. However the new Big Data paradigm has created a new environment where data can be potentially non-finite and arrive continuously. Such streams of data can reach computing systems at high speeds and contain data generation processes which might be non-stationary. For clustering tasks, this implies inconceivability to store all information in memory and obscure number and size of clusters. Noise levels can also be high due to either data generation or transmission. All these factors make traditional clustering methods not suitable to cope. As a consequence, stream clustering has emerged as a field of intense research with the aim of tackling these challenges. Clustream is one of the most advanced state of the art stream clustering algorithm. It normally requires two phases: first online micro-clustering phase, where statistics are gathered describing the incoming data; and a second offline macro-clustering phase, where a conventional non-stream clustering algorithm is executed using the high level statistics resulting from the online step. Because of its design, it requires expert-level parametrization or suffers from low runtime performance or has high sensitivity to noise or degrade considerably in high dimensional spaces because of their offline step. We propose a new stream clustering algorithm, the Clustream-hybrid based on Clustream clustering principles. It extends the same process used in Clustream but uses k-means++ instead of k-means in macro-clustering phase enabling it to accomplish quick runtime calculation while additionally keeping accuracy in high dimensional settings. We integrate it in MOA (Massive Online Analysis) tool. We evaluated the results with nine clustering quality metrics and compared the performance with Clustream for both synthetic and real data sets. The results are encproposedaging, outperforming in most of the cases in quality metrics.

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.

<p>Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a division strategy (KDG) is proposed to divide the cell of grid adaptively. Secondly, an improve fruit fly optimization algorithm (IFOA) which use the step strategy based on knowledge learn (KLSS) and the clustering criterion function (CFF) is designed. In addition, based on IFOA algorithm, the optimal parameters of local clustering are dynamically selected, which can improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, the density-based clustering algorithm using IFOA (MR-QRMEC) are proposed to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce, a cluster merging algorithm (MR-QRMEC) is proposed to get the result of clustering algorithm more quickly, which improve the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA algorithm has better clustering results and performs better parallelization in big data.</p> <p> </p>

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.

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.

A number of clustering algorithms can be employed to find clusters in multivariate data. However, the effectiveness and efficiency of the existing algorithms are limited, since the respective data has high dimension, contain large amount of noise and consist of clusters with arbitrary shapes and densities. In this paper, a new kernel density-based clustering algorithm, called Local Triangular Kernel-based Clustering (LTKC), is proposed to deal with these conditions. LTKC is based on combination of k-nearest-neighbor density estimation and triangular kernel density-based clustering. The advantages of our LTKC approach are: (1) it has a firm mathematical basis; (2) it requires only one parameter, number of neighbors; (3) it defines the number of cluster automatically; (4) it allows discovering clusters with arbitrary shapes and densities ;and (5) it is significantly faster than existing algorithms. LTKC is tested using artificial data and applied to some UCI data. A comparison with k-means, KFCM and well known density-based clustering algorithms including ILGC, DBSCAN, and DENCLUE shows the superiority of our proposed LTKC algorithm.

© 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.

Stochastic Gradient Descent (SGD) is widely used to train a machine learning model over large datasets, yet its slow convergence rate can be a bottleneck. As a remarkable family of variance reduction techniques, memory algorithms such as SAG and SAGA have been proposed to accelerate the convergence rate of SGD. However, these algorithms need to store per training data point corrections in memory. The unlimited space usage feature is impractical for modern large-scale applications, especially over data points that arrive over time (referred to as streaming data in this paper). To overcome this weakness, this paper investigates the methods that bound the space usage in the state-of-the-art family of variance-reduced stochastic gradient descent over streaming data, and presents CHEAPS2AGA. At each step of updating the model, the key idea of CHEAPS2AGA is always reserving N random data points as samples, while re-using information about past stochastic gradients across all the observed data points with limited space usage. In addition, training an accurate model over streaming data requires the algorithm to be time-efficient. To accelerate the model training phase, CHEAPS2AGA embraces a lock-free data structure to insert new data points and remove unused data points in parallel, and updates the model parameters without using any locking. We conduct comprehensive experiments to compare CHEAPS2AGA to prior related algorithms suited for streaming data. The experimental results demonstrate the practical competitiveness of CHEAPS2AGA in terms of scalability and accuracy.

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.

This paper presents a clustering algorithm based on Voronoi diagrams. The algorithm firstly constructs irregular grids in plane by Voronoi diagrams, then assign the points among different grids to different clusters according to the property of the Voronoi diagrams' “the nearest neighbor”. It is able to automatically modify the final clustering number based on the grid points' density, and it can adjust the locations for the Voronoi's seeds by the changes of the centroids, and the final Voronoi cells becomes the clustering result. The algorithm is able to settle down the clustering numbers automatically and also can recognize the low density points automatically. The experiments prove that the algorithm can cluster effectively the data points in plane, and its performance is similar to the X-means algorithm which is improved on the K-means algorithm. It is more effective than the DBSCAN and the OPTICS which are density-based clustering algorithms. The algorithm proved to be obviously more effective while the experimental data is in a larger scale.

Data streams can capture the situation which an actor is experiencing. Knowledge of the present situation is highly beneficial for a wide range of applications. An algorithm called pcStream can be used to extract situations from a numerical data stream in an unsupervised manner. Although pcStream outperforms other stream clustering algorithms at this task, pcStream has two major flaws. The first is its complexity due to continuously performing principal component analysis (PCA). The second is its difficulty in detecting emerging situations whose distributions overlap in the same feature space. In this paper we introduce pcStream2, a variant of pcStream which employs windowing and persistence in order to distinguish between emerging overlapping concepts. We also propose the use of incremental PCA (IPCA) to reduce the overall complexity and memory requirements of the algorithm. Although any IPCA algorithm can be used, we use a novel IPCA algorithm called Just-In-Time PCA which is better suited for processing streams. JIT-PCA makes intelligent 'short cuts' in order to reduce computations. We provide experimental results on real-world datasets that demonstrates how the proposed improvements make pcStream2 a more accurate and practical tool for situation space mining.

Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main hub cities must be clustered using cluster analysis, and then divided level in order to functional analyze. K-means algorithm is the most widely used algorithm in clustering analysis, which clustering numbers and initial clustering centers are uncertain. This paper proposes application of K-means algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm. The experimental results show this algorithm can more effectively solved clustering problem than K-means algorithm and ant clustering algorithm.

The Ant Clustering Algorithm (ACA) is a biological inspired data clustering technique, which aimed to cluster and classify the data patterns into different groups. This paper shows how the Ant Clustering Algorithm (ACA) is implemented in clustering and classifying the tropical wood species. As for feature extraction in this research, two feature extractors are selected to extract wood features from wood images, which are Basic Grey Level Aura Matrices (BGLAM) and Statistical Properties of Pores Distribution (SPPD). The ACA algorithm is then been applied in wood data training and testing, and as a result, it is proven that the ACA algorithm can cluster and classify the tropical wood data accurately and effectively.