Parallel complete gradient clustering algorithm and its properties

Kowalski, Piotr A.; Jeczmionek, Ernest

doi:10.1016/j.ins.2022.03.087

Cited by 9 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to equation ( 3 ), the Lagrange multiplier is denoted by, and the formalized term derived from it is denoted by E. Following the deduction, it's usual to find that the proper mathematical equation for z n is:

…”

Section: Methods Of Multiview Clusteringmentioning

confidence: 99%

“…'Exploratory Data Analysis (EDA) is a field in which clustering is a significant component. It dissects the interrelationships between the various data properties, breaking them down into more manageable chunks [Kowalski et al [ 3 ]]. For Tensor Train (TT) and Tensor flow Ring (TR) also known as “Tensor Chain” decompositions, the optimum rank selection is an essential topic.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tensor Multi-Clustering Parallel Intelligent Computing Method Based on Tensor Chain Decomposition

Zhang

Fan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Adaptable methods for representing higher-order data with various features and high dimensionality have been demanded by the increasing usage of multi-sensor technologies and the emergence of large data sets. Arrays of multi-dimensional data, known as tensors, can be found in a variety of applications. Standard data that depicts things from a single point of view lacks the semantic richness, utility, and complexity of multi-dimensional data. Research into multi-clustering has taken off since traditional clustering methods are unable to handle large datasets. There are three main kinds of multi-clustering algorithms: Self-weighted Multiview Clustering (SwMC), Latent Multi-view Subspace Clustering (LMSC), and Multi-view Subspace Clustering with Intactness-Aware Similarity (MSC IAS) that are explored in this paper. To evaluate their performance, we do in-depth tests on seven real-world datasets. The three most important metrics Accuracy (ACC), normalized mutual information (NMI), and purity are grouped. Furthermore, traditional Principal Component Analysis (PCA) cannot uncover hidden components within multi-dimensional data. For this purpose, tensor decomposition algorithms have been presented that are flexible in terms of constraint selection and extract more broad latent components. In this examination, we also go through the various tensor decomposition methods, with an emphasis on the issues that classical PCA is designed to solve. Various tensor models are also tested for dimensionality reduction and supervised learning applications in the experiments presented here.

show abstract

…”

Section: Methods Of Multiview Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tensor Multi-Clustering Parallel Intelligent Computing Method Based on Tensor Chain Decomposition

Zhang

Fan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The existing clustering methods are usually divided into density-based, hierarchical, graph decomposition and partitioning methods. The density-based algorithm is represented by DB-SCAN (Shinde et al, 2022 ) CGCA (Kowalski and Jeczmionek, 2022 ) and other similar methods. Hierarchical clustering methods can use two different strategies : top-down and bottom-up (also known as agglomerative clustering) (Kordos et al, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Cao

Liu

et al. 2023

Front. Neurorobot.

View full text Add to dashboard Cite

Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.

show abstract

“…This technique consists of categorizing unlabeled data into groups called clusters, whose members are similar to each other and different from members of other clusters, based on the characteristics analyzed. The cluster methods are increasingly being used for several applications [6][7][8][9]. As presented in [10,11], state-of-the-art clustering methods can be inspired by the behaviors of animals.…”

Section: Introductionmentioning

confidence: 99%

Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data

Wilbert,

Hoppe,

Sartori

et al. 2023

Algorithms

View full text Add to dashboard Cite

While there are several ways to identify customer behaviors, few extract this value from information already in a database, much less extract relevant characteristics. This paper presents the development of a prototype using the recency, frequency, and monetary attributes for customer segmentation of a retail database. For this purpose, the standard K-means, K-medoids, and MiniBatch K-means were evaluated. The standard K-means clustering algorithm was more appropriate for data clustering than other algorithms as it remained stable until solutions with six clusters. The evaluation of the clusters’ quality was obtained through the internal validation indexes Silhouette, Calinski Harabasz, and Davies Bouldin. When consensus was not obtained, three external validation indexes were applied: global stability, stability per cluster, and segment-level stability across solutions. Six customer segments were obtained, identified by their unique behavior: lost customers, disinterested customers, recent customers, less recent customers, loyal customers, and best customers. Their behavior was evidenced and analyzed, indicating trends and preferences. The proposed method combining recency, frequency, monetary value (RFM), K-means clustering, internal indices, and external indices achieved return rates of 17.50%, indicating acceptable selectivity of the customers.

show abstract

Parallel complete gradient clustering algorithm and its properties

Cited by 9 publications

References 34 publications

Tensor Multi-Clustering Parallel Intelligent Computing Method Based on Tensor Chain Decomposition

Tensor Multi-Clustering Parallel Intelligent Computing Method Based on Tensor Chain Decomposition

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data

Contact Info

Product

Resources

About