Aiming to IDS (Intrusion Detection Systems) low features alarm clustering quality and excessive redundant alarms, an IDS alerts clustering algorithm based on novel chaotic particle swarm optimization is proposed. We f...
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Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K...
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Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.
Our aim was to automatically and appropriately classify Japanese psychomimes such as 'ukiuki' and 'wakuwaku'. Such terms are important because they represent users' emotions and have multiple meani...
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In this paper, we focus on the development of two similarity measure based robust possibilistic cmeans clustering (RPCM) algorithms which are not sensitive to the selection of initial parameters, robust to noise and o...
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In this paper, we focus on the development of two similarity measure based robust possibilistic cmeans clustering (RPCM) algorithms which are not sensitive to the selection of initial parameters, robust to noise and outliers, and able to automatically determine the number of clusters. The proposed algorithms are based on two different objective functions of PCM which can be regarded as special cases of similarity based robust clustering algorithms. The robustness of the proposed RPCM algorithms to noise and outliers is analyzed by using influence function and gross error sensitivity. Several simulations are conducted to demonstrate the effectiveness of the proposed algorithms.
Data mining is the process of discovering knowledge from the vast data sources. In Data mining, classification and clustering are the two broad branches of study. In clustering, K-means algorithm is one of the bench m...
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Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety o...
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Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety of clustering algorithms have been developed for scRNA-seq data. These algorithms generate cell label sets that assign each cell to a cluster. However, different algorithms usually yield different label sets, which can introduce variations in cell-type identification based on the generated label sets. Currently, the performance of these algorithms has not been systematically evaluated in single-cell transcriptome studies. Herein, we performed a critical assessment of seven state-of-the-art clustering algorithms including four deep learning-based clustering algorithms and commonly used methods Seurat, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL) and Single-cell consensus clustering (SC3). We used diverse evaluation indices based on 10 different scRNA-seq benchmarks to systematically evaluate their clustering performance. Our results show that CosTaL, Seurat, Deep Embedding for Single-cell clustering (DESC) and SC3 consistently outperformed Single-Cell clustering Assessment Framework and scDeepCluster based on nine effectiveness scores. Notably, CosTaL and DESC demonstrated superior performance in clustering specific cell types. The performance of the single-cell Variational Inference tools varied across different datasets, suggesting its sensitivity to certain dataset characteristics. Notably, DESC exhibited promising results for cell subtype identification and capturing cellular heterogeneity. In addition, SC3 requires more memory and exhibits slower computation speed compared to other algorithms for the same dataset. In sum, this study provides useful guidance for selecting appropriate clustering methods in scRNA-seq data analysis.
clustering refers to a group of unsupervised classification techniques which, generally, only rely on the available data patterns to infer groups of similar patterns. Among the many available clustering algorithms the...
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This paper concerns the usefulness of immune mechanisms for clustering tasks from the perspective of data mining. We first present a review of existing immune inspired clustering algorithms. Then these algorithms are ...
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This paper concerns the usefulness of immune mechanisms for clustering tasks from the perspective of data mining. We first present a review of existing immune inspired clustering algorithms. Then these algorithms are compared with a traditional clustering algorithm, BIRCH, which produces micro-clusters that are very similar to antibodies. Based on this comparison, the particular immune learning mechanisms are discovered. But current implementation of these mechanisms makes the algorithms need multiple scans and large memory. Thus the algorithms cannot process large databases.
In the field of online learning, the development of learning objects (LOs) has been increased. LOs promote reusing and referencing educational content in various learning environments. However, despite this progress, ...
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Advanced Persistent Threat (APT) attack has become one of the most complex attacks. It targets sensitive information. Many cybersecurity systems have been developed to detect the APT attack from network data traffic a...
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Advanced Persistent Threat (APT) attack has become one of the most complex attacks. It targets sensitive information. Many cybersecurity systems have been developed to detect the APT attack from network data traffic and request. However, they still need to be improved to identify this attack effectively due to its complexity and slow move. It gets access to the organizations either from an active directory or by gaining remote access, or even by targeting the Domain Name Server (DNS). Nowadays, many machine learning (ML) techniques have been implemented to detect APT attack by using the tools in the market. However, still, there are some limitations in terms of accuracy, efficiency, and effectiveness, especially the lack of labeled data to train ML methods. This paper proposes a framework to detect APT attacks using the most applicable clustering algorithms, such as the APRIORI, K-means, and Hunt's algorithm. To evaluate and compare the performance of the proposed framework, several experiments are conducted on a public dataset. The experimental results showed that the Support Vector Machine with Radial Basis Function (SVM-RBF) achieves the highest accuracy rate, reaching about 99.2%. This accurate result confirms the effectiveness of the developed framework for detecting attacks from network data traffic.
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