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  • Open Access


    Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities

    Ahmed Fahim1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3695-3712, 2023, DOI:10.32604/cmc.2023.036820

    Abstract Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: epsilon (fixed neighborhood radius) and MinPts (the lowest number of objects in epsilon). However, it can’t handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so… More >

  • Open Access


    Power Incomplete Data Clustering Based on Fuzzy Fusion Algorithm

    Yutian Hong1,*, Yuping Yan2

    Energy Engineering, Vol.120, No.1, pp. 245-261, 2023, DOI:10.32604/ee.2022.022877

    Abstract With the rapid development of the economy, the scale of the power grid is expanding. The number of power equipment that constitutes the power grid has been very large, which makes the state data of power equipment grow explosively. These multi-source heterogeneous data have data differences, which lead to data variation in the process of transmission and preservation, thus forming the bad information of incomplete data. Therefore, the research on data integrity has become an urgent task. This paper is based on the characteristics of random chance and the Spatio-temporal difference of the system. According to the characteristics and data… More >

  • Open Access


    Profiling Astronomical Objects Using Unsupervised Learning Approach

    Theerapat Sangpetch1, Tossapon Boongoen1,*, Natthakan Iam-On2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1641-1655, 2023, DOI:10.32604/cmc.2023.026739

    Abstract Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields. Instead of a manual inspection, various automated systems are invented to satisfy the need, including the classification of light curve profiles. A specific Kaggle competition, namely Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope (LSST) project. Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined… More >

  • Open Access


    Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

    Ashutosh Kumar Dubey1,*, Umesh Gupta2, Sonal Jain2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4523-4543, 2022, DOI:10.32604/cmc.2022.021148

    Abstract This study aims to empirically analyze teaching-learning-based optimization (TLBO) and machine learning algorithms using k-means and fuzzy c-means (FCM) algorithms for their individual performance evaluation in terms of clustering and classification. In the first phase, the clustering (k-means and FCM) algorithms were employed independently and the clustering accuracy was evaluated using different computational measures. During the second phase, the non-clustered data obtained from the first phase were preprocessed with TLBO. TLBO was performed using k-means (TLBO-KM) and FCM (TLBO-FCM) (TLBO-KM/FCM) algorithms. The objective function was determined by considering both minimization and maximization criteria. Non-clustered data obtained from the first phase… More >

  • Open Access


    A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

    Farzaneh Khorasani1, Morteza Mohammadi Zanjireh1,*, Mahdi Bahaghighat1, Qin Xin2

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 1085-1098, 2022, DOI:10.32604/csse.2022.016003

    Abstract With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected… More >

  • Open Access


    Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

    Ayat Alrosan1, Waleed Alomoush2, Mohammed Alswaitti3,*, Khalid Alissa4, Shahnorbanun Sahran5, Sharif Naser Makhadmeh6, Kamal Alieyan7

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1575-1593, 2021, DOI:10.32604/cmc.2021.015925

    Abstract Fuzzy C-means (FCM) is a clustering method that falls under unsupervised machine learning. The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres. Artificial Bee Colony (ABC) is a type of swarm algorithm that strives to improve the members’ solution quality as an iterative process with the utilization of particular kinds of randomness. However, ABC has some weaknesses, such as balancing exploration and exploitation. To improve the exploration process within the ABC algorithm, the mean artificial bee colony (MeanABC) by its modified search equation that depends… More >

  • Open Access


    Impact of Distance Measures on the Performance of AIS Data Clustering

    Marta Mieczyńska1,*, Ireneusz Czarnowski2

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 69-82, 2021, DOI:10.32604/csse.2021.014327

    Abstract Automatic Identification System (AIS) data stream analysis is based on the AIS data of different vessel’s behaviours, including the vessels’ routes. When the AIS data consists of outliers, noises, or are incomplete, then the analysis of the vessel’s behaviours is not possible or is limited. When the data consists of outliers, it is not possible to automatically assign the AIS data to a particular vessel. In this paper, a clustering method is proposed to support the AIS data analysis, to qualify noises and outliers with respect to their suitability, and finally to aid the reconstruction of the vessel’s trajectory. In… More >

  • Open Access


    Application of FCM Algorithm Combined with Articial Neural Network in TBM Operation Data

    Jingyi Fang1, Xueguan Song2, Nianmin Yao3, Maolin Shi2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 397-417, 2021, DOI:10.32604/cmes.2021.012895

    Abstract Fuzzy clustering theory is widely used in data mining of full-face tunnel boring machine. However, the traditional fuzzy clustering algorithm based on objective function is difficult to effectively cluster functional data. We propose a new Fuzzy clustering algorithm, namely FCM–ANN algorithm. The algorithm replaces the clustering prototype of the FCM algorithm with the predicted value of the articial neural network. This makes the algorithm not only satisfy the clustering based on the traditional similarity criterion, but also can effectively cluster the functional data. In this paper, we rst use the t-test as an evaluation index and apply the FCM–ANN algorithm… More >

  • Open Access


    A Direct Data-Cluster Analysis Method Based on Neutrosophic Set Implication

    Sudan Jha1, Gyanendra Prasad Joshi2, Lewis Nkenyereya3, Dae Wan Kim4, *, Florentin Smarandache5

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1203-1220, 2020, DOI:10.32604/cmc.2020.011618

    Abstract Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University of California, Irvine, Machine… More >

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