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HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter mpts. While the performance of HDBSCAN* is robust w.r.t. mpts, choosing a "good" value for it can be challenging: depending on the data distribution, a high or low value for mpts may be more appropriate, and certain data clusters may...
Clustering is an important branch in the field of data mining as well as statistical analysis and is widely used in exploratory analysis. Many algorithms exist for clustering in the Euclidean space. However, time series clustering introduces new problems, such as inadequate distance measure, inaccurate cluster center description, lack of efficient and accurate clustering techniques. When dealing with...
An important research topic of the recent years has been to understand and analyze manifold-modeled data for clustering and classification applications. Most clustering methods developed for data of non-linear and low-dimensional structure are based on local linearity assumptions. However, clustering algorithms based on locally linear representations can tolerate difficult sampling conditions only...
Collaborative learning is widely accepted as an approach to promote learning effectiveness and student satisfaction. However, the quality and outcomes of collaboration depend upon a number of factors, among which group formation plays an important role. Existing approaches take into account groups formed through random assignment or based on certain criteria such as academic performance, demographic...
Selection of methods will greatly impact in learning process. One of the methods commonly applied are Cooperative learning. Cooperative learning is one of many learning techniques to improve the performance of students in the academic literature. Moreover, the heterogeneity in study group's academics can improve performance, but only partially implementing cooperative learning in a group of heterogeneous...
Image compression means reducing the size of bytes of the file which allows the user to store a higher amount of data within a fixed memory. Our paper discusses a new proposed compression algorithm to perform lossy image compression by implementing the clustering approach using the Euclidean distance method. The main objective is to minimize the storage needs by realizing the closest mean pixel data...
Data clustering methods have been used extensively for image segmentation in the past decade. In our previous work, we had established that combining the traditional clustering algorithms with a meta-heuristic like Firefly Algorithm improves the stability of the output as well as the speed of convergence. In this paper, we have replaced the Euclidean distance formula with kernels. We have combined...
In a large-scale indoor environment, a mobile robot needs a proper internal representation of the surrounding environment to carry out its tasks. The metric (grid-based) map and topological map are two common internal representations in robotic realm. In order to take advantage of the two kinds of environmental representations, this paper aims to construct a topological map of an indoor environment...
Video summarization system can yield good results if the high level features also called the semantic concepts in video frame are modeled accurately by considering the temporal aspects of the frames. The existing system is context aware surveillance video summarization which is a Domain dependent System. It works only on low level features and correlation between them is extracted and updated using...
In this paper, a novel spectral-spatial low-rank subspace clustering (SS-LRSC) algorithm is presented for clustering of hyperspectral images (HSI). Generally, employing the traditional LRSC framework directly cannot fully exploit the sample correlations in original spatial domain. Therefore, the proposed method utilizes a novel modulation strategy to modify the low rank representation matrix, which...
Eye movement reflects the shift of overt visual attention. Eye movement trajectories from a group of observers can be expressed by a representative scanpath. The representative scan-path can work as a baseline for studies on scanpath prediction as well as provide useful knowledge about group behavior in psychological studies. In this paper, we propose a new framework to summarize a representative...
Topics on clustering ensemble have attracted much attention in recent years. In many clustering ensemble frameworks, the simple partitional clustering methods, e.g., the most famous κ-means, are used as the ensemble's member “clusterers”, due to their low computational complexity. These ensemble approaches extend the scope of application of individual clustering algorithms, and improve the robustness...
Kernel partial least squares(KPLS) is widely adopted for soft-sensing in nonlinear industrial process. For KPLS method, the determination of central nodes and kernel width in the kernel function will affects generalization ability and predictiability. This paper proposes an entropy-clustering and K-means based KPLS regression method. First of all, it divides the original data into several clusters...
Cross domain data such as numerical or categorical types are ubiquitous in practical network. Network anomaly detection based on cluster analysis exist some difficulties, for example, the initial center of cluster analysis is sensitive and easy to fall into the local optimal solution. Cross domain data involved great information, but can't be effectively used, which will influence the performance...
Mining big data often requires tremendous computational resources. This has become a major obstacle to broad applications of big data analytics. Cloud computing allows data scientists to access computational resources on-demand for building their big data analytics solutions in the cloud. However, the monetary cost of mining big data in the cloud can still be unexpectedly high. For example, running...
In this article we propose a relational and a median possibilistic clustering method. Both methods are modifications of Possibilistic Fuzzy C-Means as introduced by Pal et al. [1]. The proposed algorithms are applicable for abstract non-vectorial data objects where only the dissimilarities of the objects are known. For the relational version we assume a Euclidean data embedding. For data where this...
Battery clustering is to sort out homogeneous battery cells to form a battery pack with high uniformity, which is of great importance to prolong the cycle life of the lithium-ion battery. The traditional method for battery clustering is to compare the charge and discharge characteristic curves of the battery cells. This paper proposes a new algorithm, the squeeze algorithm, for a fast testing and...
Density based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes in the statistical properties of the data. However, most density approaches require sensitive, data dependent parameters. These parameters greatly affect the clustering performance...
This paper presents a new differential evolution algorithm for multimodal optimization that uses self-adaptive parameter control, clustering and crowding methods. The algorithm includes a new clustering mechanism that is based on small subpopulations with the best strategy and, as such, improves the algorithm's efficiency. Each subpopulation is generated according to the best individual from a population...
k-Means clustering algorithm is widely used in many machine learning tasks. However, the classic k-Means clustering algorithm has poor performance on classification of non-convex data sets. We find that k-Means effect depends heavily on the measurement of similarity between instances of the datasets. In novel algorithm, we define the new distance measurement of scalable spatial density similarity...
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