scholarly journals Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
JingDong Tan ◽  
RuJing Wang
Keyword(s):  
Graph Theory ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Clustering Methods ◽  
Single Edge ◽  
Density Based Clustering ◽  
Measure Of Similarity

Sharing nearest neighbor (SNN) is a novel metric measure of similarity, and it can conquer two hardships: the low similarities between samples and the different densities of classes. At present, there are two popular SNN similarity based clustering methods: JP clustering and SNN density based clustering. Their clustering results highly rely on the weighting value of the single edge, and thus they are very vulnerable. Motivated by the idea of smooth splicing in computing geometry, the authors design a novel SNN similarity based clustering algorithm within the structure of graph theory. Since it inherits complementary intensity-smoothness principle, its generalizing ability surpasses those of the previously mentioned two methods. The experiments on text datasets show its effectiveness.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

An improved OPTICS clustering algorithm for discovering clusters with uneven densities

2021 ◽  
Vol 25 (6) ◽  
pp. 1453-1471
Author(s):  
Chunhua Tang ◽  
Han Wang ◽  
Zhiwen Wang ◽  
Xiangkun Zeng ◽  
Huaran Yan ◽  
...  
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Substantial Improvement ◽  
Experimental Results ◽  
High Time ◽  
Parameter Setting ◽  
K Nearest Neighbor ◽  
Density Based Clustering

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.

scholarly journals Scalable hierarchical clustering by composition rank vector encoding and tree structure

2020 ◽  
Author(s):  
Xiao Lai ◽  
Pu Tian
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
Machine Learning Algorithms ◽  
Tree Structure ◽  
Supervised Machine Learning ◽  
High Dimensional ◽  
Rank Vector ◽  
Nonlinear Correlations

AbstractSupervised machine learning, especially deep learning based on a wide variety of neural network architectures, have contributed tremendously to fields such as marketing, computer vision and natural language processing. However, development of un-supervised machine learning algorithms has been a bottleneck of artificial intelligence. Clustering is a fundamental unsupervised task in many different subjects. Unfortunately, no present algorithm is satisfactory for clustering of high dimensional data with strong nonlinear correlations. In this work, we propose a simple and highly efficient hierarchical clustering algorithm based on encoding by composition rank vectors and tree structure, and demonstrate its utility with clustering of protein structural domains. No record comparison, which is an expensive and essential common step to all present clustering algorithms, is involved. Consequently, it achieves linear time and space computational complexity hierarchical clustering, thus applicable to arbitrarily large datasets. The key factor in this algorithm is definition of composition, which is dependent upon physical nature of target data and therefore need to be constructed case by case. Nonetheless, the algorithm is general and applicable to any high dimensional data with strong nonlinear correlations. We hope this algorithm to inspire a rich research field of encoding based clustering well beyond composition rank vector trees.

scholarly journals A Novel High-Dimensional Trajectories Construction Network based on Multi-Clustering Algorithm

2021 ◽  
Author(s):  
Feiyang Ren ◽  
Yi Han ◽  
Shaohan Wang ◽  
He Jiang
Keyword(s):  
Economic Analysis ◽  
Clustering Algorithm ◽  
Transportation Network ◽  
High Dimensional ◽  
Clustering Methods ◽  
Marine Transportation ◽  
Network Construction ◽  
National Economic ◽  
Multi Level ◽  
State Of Art

Abstract A novel marine transportation network based on high-dimensional AIS data with a multi-level clustering algorithm is proposed to discover important waypoints in trajectories based on selected navigation features. This network contains two parts: the calculation of major nodes with CLIQUE and BIRCH clustering methods and navigation network construction with edge construction theory. Unlike the state-of-art work for navigation clustering with only ship coordinate, the proposed method contains more high-dimensional features such as drafting, weather, and fuel consumption. By comparing the historical AIS data, more than 220,133 lines of data in 30 days were used to extract 440 major nodal points in less than 4 minutes with ordinary PC specs (i5 processer). The proposed method can be performed on more dimensional data for better ship path planning or even national economic analysis. Current work has shown good performance on complex ship trajectories distinction and great potential for future shipping transportation market analytical predictions.

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