Relative density-based clustering algorithm for identifying diverse density clusters effectively

2021 ◽  
Author(s):  
Yuying Wang ◽  
Youlong Yang
Keyword(s):  
Relative Density ◽  
Clustering Algorithm ◽  
Density Based Clustering ◽  
Diverse Density

scholarly journals Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Luo Xuegang ◽  
Lv Junrui ◽  
Wang Juan
Keyword(s):  
Relative Density ◽  
Missing Values ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Sensor Data ◽  
Low Rank ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Norm Minimization ◽  
Density Based Clustering

An effective fraction of data with missing values from various physiochemical sensors in the Internet of Things is still emerging owing to unreliable links and accidental damage. This phenomenon will limit the predicative ability and performance for supporting data analyses by IoT-based platforms. Therefore, it is necessary to exploit a way to reconstruct these lost data with high accuracy. A new data reconstruction method based on spectral k-support norm minimization (DR-SKSNM) is proposed for NB-IoT data, and a relative density-based clustering algorithm is embedded into model processing for improving the accuracy of reconstruction. First, sensors are grouped by similar patterns of measurement. A relative density-based clustering, which can effectively identify clusters in data sets with different densities, is applied to separate sensors into different groups. Second, based on the correlations of sensor data and its joint low rank, an algorithm based on the matrix spectral k-support norm minimization with automatic weight is developed. Moreover, the alternating direction method of multipliers (ADMM) is used to obtain its optimal solution. Finally, the proposed method is evaluated by using two simulated and real sensor data sources from Panzhihua environmental monitoring station with random missing patterns and consecutive missing patterns. From the simulation results, it is proved that our algorithm performs well, and it can propagate through low-rank characteristics to estimate a large missing region’s value.

Adaptive Density-Based Clustering Algorithm with Shared KNN Conflict Game

2021 ◽  
Author(s):  
Rui Zhang ◽  
Tao Du ◽  
Shouning Qu ◽  
Hongwei Sun
Keyword(s):  
Clustering Algorithm ◽  
Density Based Clustering

Distance Density Based Clustering Algorithm in Wireless Sensor Network

2011 ◽  
Vol 291-294 ◽  
pp. 344-348
Author(s):  
Lin Lin ◽  
Shu Yan ◽  
Yi Nian
Keyword(s):  
Clustering Algorithm ◽  
Distribution Density ◽  
Simulation Experiment ◽  
Clustering Algorithms ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Cluster Heads ◽  
Hierarchical Topology ◽  
Energy Factors ◽  
Density Based Clustering

The hierarchical topology of wireless sensor networks can effectively reduce the consumption in communication. Clustering algorithm is the foundation to realize herarchical structure, so it has been extensive researched. On the basis of Leach algorithm, a distance density based clustering algorithm (DDBC) is proposed, considering synthetically the distribution density of around nodes and the remaining energy factors of the node to dynamically banlance energy usage of nodes when selecting cluster heads. We analyzed the performance of DDBC through compared with the existing other clustering algorithms in simulation experiment. Results show that the proposed method can generare stable quantity cluster heads and banlance the energy load effectively.

An Efficient Density-based Clustering Algorithm for Face Groping

2021 ◽  
Author(s):  
Shenfei Pei ◽  
Feiping Nie ◽  
Rong Wang ◽  
Xuelong Li
Keyword(s):  
Clustering Algorithm ◽  
Density Based Clustering

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 A new method for unveiling open clusters in Gaia

2018 ◽  
Vol 618 ◽  
pp. A59 ◽  
Author(s):  
A. Castro-Ginard ◽  
C. Jordi ◽  
X. Luri ◽  
F. Julbe ◽  
M. Morvan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Dimensional Space ◽  
Open Clusters ◽  
Machine Learning Algorithms ◽  
New Era ◽  
Use Of Data ◽  
Manual Intervention ◽  
Density Based Clustering ◽  
Artificial Neural Network Ann ◽  
Astrometric Data

Context. The publication of the Gaia Data Release 2 (Gaia DR2) opens a new era in astronomy. It includes precise astrometric data (positions, proper motions, and parallaxes) for more than 1.3 billion sources, mostly stars. To analyse such a vast amount of new data, the use of data-mining techniques and machine-learning algorithms is mandatory. Aims. A great example of the application of such techniques and algorithms is the search for open clusters (OCs), groups of stars that were born and move together, located in the disc. Our aim is to develop a method to automatically explore the data space, requiring minimal manual intervention. Methods. We explore the performance of a density-based clustering algorithm, DBSCAN, to find clusters in the data together with a supervised learning method such as an artificial neural network (ANN) to automatically distinguish between real OCs and statistical clusters. Results. The development and implementation of this method in a five-dimensional space (l, b, ϖ, μα*, μδ) with the Tycho-Gaia Astrometric Solution (TGAS) data, and a posterior validation using Gaia DR2 data, lead to the proposal of a set of new nearby OCs. Conclusions. We have developed a method to find OCs in astrometric data, designed to be applied to the full Gaia DR2 archive.

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