Density Based Clustering Methods for Road Traffic Estimation

2020 ◽  
Author(s):  
Jagadish D. N ◽  
Lakshman Mahto ◽  
Arun Chauhan
Keyword(s):  
Road Traffic ◽  
Clustering Methods ◽  
Density Based Clustering ◽  
Traffic Estimation

Road-RFSense: A Practical RF Sensing--Based Road Traffic Estimation System for Developing Regions

2014 ◽  
Vol 11 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Rijurekha Sen ◽  
Abhinav Maurya ◽  
Bhaskaran Raman ◽  
Rupesh Mehta ◽  
Ramkrishnan Kalyanaraman ◽  
...  
Keyword(s):  
Road Traffic ◽  
Developing Regions ◽  
Traffic Estimation ◽  
Estimation System ◽  
Rf Sensing

scholarly journals Evolutionary Algorithms for Robust Density-Based Data Clustering

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Amit Banerjee
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Computation ◽  
Data Clustering ◽  
Relational Data ◽  
Clustering Methods ◽  
Density Based Clustering ◽  
Selection Of

Density-based clustering methods are known to be robust against outliers in data; however, they are sensitive to user-specified parameters, the selection of which is not trivial. Moreover, relational data clustering is an area that has received considerably less attention than object data clustering. In this paper, two approaches to robust density-based clustering for relational data using evolutionary computation are investigated.

scholarly journals Density-based classification with the DENCLUE algorithm

2021 ◽  
Vol 24 (1) ◽  
pp. 269
Author(s):  
Mouhcine El Hassani ◽  
Noureddine Falih ◽  
Belaid Bouikhalene
Keyword(s):  
Spatial Clustering ◽  
Data Partitioning ◽  
Clustering Methods ◽  
Data Set ◽  
Density Based Clustering ◽  
Marketing Data ◽  
Numerous Data ◽  
Bank Marketing ◽  
Probabilistic Nature

<p><span>Classification of information is a vague and difficult to explore area of research, hence the emergence of grouping techniques, often referred to Clustering. It is necessary to differentiate between an unsupervised and a supervised classification. Clustering methods are numerous. Data partitioning and hierarchization push to use them in parametric form or not. Also, their use is influenced by algorithms of a probabilistic nature during the partitioning of data. The choice of a method depends on the result of the Clustering that we want to have. This work focuses on classification using the density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithm through an application made in csharp. Through the use of three databases which are the IRIS database, breast cancer wisconsin (diagnostic) data set and bank marketing data set, we show experimentally that the choice of the initial data parameters is important to accelerate the processing and can minimize the number of iterations to reduce the execution time of the application.</span></p>

scholarly journals Application of Density Based Clustering of Disaster Location in Realtime Social Media

TEM Journal ◽  
2020 ◽  
pp. 929-936
Author(s):  
Mochammad Haldi Widianto ◽  
Ivan Diryana Sudirman ◽  
Muhammad Hanif Awaluddin
Keyword(s):  
Social Media ◽  
Natural Disasters ◽  
Clustering Methods ◽  
Silhouette Coefficient ◽  
Density Based Clustering ◽  
Data Density ◽  
Disaster Data ◽  
User Data ◽  
High Degree ◽  
Degree Of Similarity

Online life is used as a method of finding information, one of which is Twitter as the medium. The occurrence of natural disasters is very detrimental. Therefore, the application is needed to see natural disasters through social media Twitter. A small number of studies using clustering methods based on Twitter user data density are the beginning of this research. With the availability of data in certain areas makes it easy to group. After that, the data is grouped based on a high degree of similarity. One result of applying this method is the location of the disaster. NER-based rules are used to discover out the area of the disaster. Data accuracy testing is performed using the Silhouette coefficient.

IMPROVED DENSITY BASED ALGORITHM FOR DATA STREAM CLUSTERING

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Maryam Mousavi ◽  
Azuraliza Abu Bakar
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Stream Clustering ◽  
Density Based Clustering ◽  
Clustering Quality ◽  
Data Stream Clustering

In recent years, clustering methods have attracted more attention in analysing and monitoring data streams. Density-based techniques are the remarkable category of clustering techniques that are able to detect the clusters with arbitrary shapes and noises. However, finding the clusters with local density varieties is a difficult task. For handling this problem, in this paper, a new density-based clustering algorithm for data streams is proposed. This algorithm can improve the offline phase of density-based algorithm based on MinPts parameter. The experimental results show that the proposed technique can improve the clustering quality in data streams with different densities.

Correlative Density-Based Clustering

2016 ◽  
Vol 13 (10) ◽  
pp. 6935-6943 ◽  
Author(s):  
Jia-Lin Hua ◽  
Jian Yu ◽  
Miin-Shen Yang
Keyword(s):  
Correlation Analysis ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Data Set ◽  
Density Based Clustering ◽  
Inherent Structure ◽  
Data Points ◽  
Artificial Datasets

Mountains, which heap up by densities of a data set, intuitively reflect the structure of data points. These mountain clustering methods are useful for grouping data points. However, the previous mountain-based clustering suffers from the choice of parameters which are used to compute the density. In this paper, we adopt correlation analysis to determine the density, and propose a new clustering algorithm, called Correlative Density-based Clustering (CDC). The new algorithm computes the density with a modified way and determines the parameters based on the inherent structure of data points. Experiments on artificial datasets and real datasets demonstrate the simplicity and effectiveness of the proposed approach.

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