Jiang Xie

Chongqing University of Posts and Telecommunications

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Guoyin Wang

Chongqing University of Posts and Telecommunications

Shuyin Xia

Chongqing University of Posts and Telecommunications

Yufang Zhang

Hebei University of Chinese Medicine

Zhongyang Xiong

Chongqing University

Qizhu Dai

Chongqing University

Xiaoxia Wang

Beijing Academy of Artificial Intelligence

Xinbo Gao

Xidian University

Dongdong Cheng

Chongqing University of Posts and Telecommunications

Jinlong Huang

Shanghai Jiao Tong University

Xuexin Xiang

Chongqing University of Posts and Telecommunications

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Chongqing University of Posts and Telecommunications

Chongqing University

University of Chinese Academy of Sciences

Xidian University

Xi'an Jiaotong University

Tsinghua University

Yangtze Normal University

University of Electronic Science and Technology of China

University of Science and Technology of China

Stanford University

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An Efficient Spectral Clustering Algorithm Based on Granular-Ball

IEEE Transactions on Knowledge and Data Engineering (2023)

Jiang Xie Weiyu Kong Shuyin Xia Guoyin Wang Xinbo Gao

In order to solve the problem that the traditional spectral clustering algorithm is time-consuming and resource consuming when applied to large-scale data, resulting in poor clustering effect or even unable to cluster, this paper proposes a spectral clustering algorithm based on granular-ball(GBSC). The algorithm changes the construction method of the similarity matrix. Based on granular-ball, the size of the similarity matrix is greatly reduced, and the construction of the similarity matrix is more reasonable. Experimental results show that the proposed algorithm achieves better speedup ratio, less memory consumption and stronger anti noise performance while achieving similar clustering results to the traditional spectral clustering algorithm. Suppose the number of granular-balls is

$m$

$n$

is the number of points in the dataset, and

$m< < n$

, the time complexity of GBSC is

$O(m^{3})$

. It is proved that GBSC has good adaptability to large-scale datasets. All codes have been released at https://github.com/xjnine/GBSC .

Ball (mathematics)

Spectral Clustering

Speedup

Similarity (geometry)

Matrix (chemical analysis)

10.1109/tkde.2023.3249475

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Citations (28)

An Efficient Fuzzy Stream Clustering Method Based on Granular-Ball Structure

2022 IEEE 38th International Conference on Data Engineering (ICDE) (2024)

Jiang Xie Minggao Dai Shuvin Xia Jinajinz Zhang Guoyin Wang

Ball (mathematics)

10.1109/icde60146.2024.00074

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Citations (2)

GB-DBSCAN: A fast granular-ball based DBSCAN clustering algorithm

Information Sciences (2024)

Dongdong Cheng Cheng Zhang Ya Li Shuyin Xia Guoyin Wang

DBSCAN

Ball (mathematics)

10.1016/j.ins.2024.120731

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Citations (4)

Research and Application of Multi-Criteria Decision Making Method based on Order Relation and Rough Set

Systems Engineering - Theory & Practice (2007)

Jiang Xie

Preference relation

Economic shortage

Decision rule

Source

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Citations (10)

A new internal index based on density core for clustering validation

Information Sciences (2019)

Jiang Xie Zhongyang Xiong Qizhu Dai Xiaoxia Wang Yufang Zhang

Single-linkage clustering

Tree (set theory)

Data set

10.1016/j.ins.2019.08.029

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Citations (32)

A Novel Silhouettes Cluster Internal Evaluation Index Based on Granular-Ball

Pengfei Zhao Zizhong Chen Jiang Xie Shuyin Xia Guoyin Wang

Cluster internal evaluation index is used to evaluate and guide the results of clustering, which has been considered as one of the vital issues in the application of clustering. Granular-ball is the multi-granularity characterization of the data set, In this paper, the classic Silhouettes indexes were improved by using granular-ball to represent the grain, we proposed a Silhouettes cluster internal evaluation index based on granular-ball(GSCVI), GSCVI can effectively obtain the optimal number of clusters for arbitrary-shaped and noisy data sets, and it is superior to most of the existing indexes for both artificial and real data sets.

Ball (mathematics)

Granularity

Granular Computing

10.1109/icccbda56900.2023.10154684

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Citations (1)

K-Means Clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters

IEEE Transactions on Neural Networks and Learning Systems (2023)

Dongdong Cheng Jinlong Huang Sulan Zhang Shuyin Xia Guoyin Wang

Due to simplicity, K-means has become a widely used clustering method. However, its clustering result is seriously affected by the initial centers and the allocation strategy makes it hard to identify manifold clusters. Many improved K-means are proposed to accelerate it and improve the quality of initialize cluster centers, but few researchers pay attention to the shortcoming of K-means in discovering arbitrary-shaped clusters. Using graph distance (GD) to measure the dissimilarity between objects is a good way to solve this problem, but computing the GD is time-consuming. Inspired by the idea that granular ball uses a ball to represent the local data, we select representatives from a local neighborhood, called natural density peaks (NDPs). On the basis of NDPs, we propose a novel K-means algorithm for identifying arbitrary-shaped clusters, called NDP-Kmeans. It defines neighbor-based distance between NDPs and takes advantage of the neighbor-based distance to compute the GD between NDPs. Afterward, an improved K-means with high-quality initial centers and GD is used to cluster NDPs. Finally, each remaining object is assigned according to its representative. The experimental results show that our algorithms can not only recognize spherical clusters but also manifold clusters. Therefore, NDP-Kmeans has more advantages in detecting arbitrary-shaped clusters than other excellent algorithms.

K-Means Clustering

Complete-linkage clustering

Ball (mathematics)

10.1109/tnnls.2023.3248064

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Citations (28)

A local-gravitation-based method for the detection of outliers and boundary points

Knowledge-Based Systems (2019)

Jiang Xie Zhongyang Xiong Qizhu Dai Xiaoxia Wang Yufang Zhang

Data point

10.1016/j.knosys.2019.105331

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