scholarly journals Evaluation the influence of distance-based K-means method for detecting moving vehicles

2022 ◽  
Vol 1212 (1) ◽  
pp. 012044
Author(s):  
Y Sari ◽  
P B Prakoso ◽  
A R Baskara
Keyword(s):  
Euclidean Distance ◽  
Processing Time ◽  
Clustering Algorithm ◽  
Moving Objects ◽  
Manhattan Distance ◽  
Intelligent Transport System ◽  
Data Set ◽  
Intelligent Transport ◽  
Moving Vehicles ◽  
Canberra Distance

Abstract Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.

scholarly journals Clustering with Euclidean Distance, Manhattan - Distance, Mahalanobis - Euclidean Distance, and Chebyshev Distance with Their Accuracy

2021 ◽  
Vol 5 (2) ◽  
pp. 369-376
Author(s):  
Said Al Afghani ◽  
Widhera Yoza Mahana Putra
Keyword(s):  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Manhattan Distance ◽  
Experiment Data ◽  
Data Grouping

There are several algorithms to solve many problems in grouping data. Grouping data is also known as clusterization, clustering takes advantage to solve some problems especially in business. In this note, we will modify the clustering algorithm based on distance principle which background of K-means algorithm (Euclidean distance). Manhattan, Mahalanobis-Euclidean, and Chebyshev distance will be used to modify the K-means algorithm. We compare the clustered  result related to their accuracy, we got Mahalanobis - Euclidean distance gives the best accuracy on our experiment data, and some results are also given in this note.

scholarly journals Pengelompokan Wilayah Madura Berdasar Indikator Pemerataan Pendidikan Menggunakan Partition Around Medoids Dan Validasi Adjusted Random Index

2015 ◽  
Vol 1 (1) ◽  
pp. 17 ◽  
Author(s):  
Budi Dwi Satoto ◽  
Bain Khusnul Khotimah ◽  
Iswati Iswati
Keyword(s):  
Data Mining ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Adjusted Rand Index ◽  
Manhattan Distance ◽  
Random Index ◽  
Original Label ◽  
Long Time ◽  
Canberra Distance

Abstrak— Pemerataan pendidikan di Indonesia telah menjadi perhatian pemerintah sejak lama. Namun hingga saat ini, pendidikan di Indonesia masih belum merata. Hal tersebut dapat dilihat dari rendahnya nilai Angka Partisisipasi Kasar (APK) dan Angka Partisipasi Murni (APM) di daerah-daerah tertentu serta belum meratanya sarana dan prasarana pendidikan. Adapun tujuan penelitian ini adalah memberikan informasi kepada pemerintah setempat mengenai kondisi pendidikan di wilayahnya sehingga dapat menghasilkan kebijakan yang tepat mengenai pengembangan infrastuktur pendidikan dan distribusi guru bantu. Clustering adalah metode data mining yang membagi data kedalam kelompok yang mempunyai objek yang karakteristik sama. Penelitian ini menggunakan metode clustering Partition Around  Medoids (PAM) dengan 3 distance measure: Manhattan, Euclidean dan Canberra distance. Untuk mengukur kualitas hasil clustering, digunakan nilai Adjusted Rand Index (ARI). Semakin besar nilai ARI, semakin baik kualitas cluster. Dari 3 kali ujicoba diperoleh rata-rata nilai ARI untuk Euclidean distance sebesar 0.799, Manhattan distance dengan rata-rata sebesar 0.738 dan Canberra distance sebesar 0.163. Sedangkan pengelompokan terbaik diperoleh menggunakan Euclidean distance dengan nilai ARI sebesar 0.825 dan kecocokan dengan label asli sebesar 83.33%. Dari pengelompokan terbaik menghasilkan kelompok pemerataan tinggi terdiri dari 11 kecamatan, kelompok pemerataan sedang  terdiri dari 15 kecamatan dan kelompok pemerataan rendah terdiri dari 46 kecamatan. Kata Kunci— indikator pemerataan pendidikan, clustering, Partition Around Medoid, distance measure, Adjusted Random IndexAbstract—Distribution of education in Indonesia has become government's attention for a long time. But until now, education in Indonesia is still not evenly distributed. This can be seen from the low value of Participation Rough figures and net enrollment ratio in certain areas as well as uneven educational facilities. The purpose of this research is to provide information to local authorities about the state of education in local region to produce an appropriate policy regarding development of educational infrastructure and teachers assistant distribution. Clustering is a data mining method that divides data into several groups with the same object characteristics. This research used Partition Around Medoids methods with 3 distance measure that contain Manhattan, Euclidean and Canberra distance. Adjusted Random Index used to measure the quality of clustering results. From 3 times sampling, better value of ARI Euclidean distance 0.799,  Manhattan distance 0.738 and Canberra distance 0.163  while the best clustering obtained  is Euclidean distance with value of ARI 0.825 and compatibility with the original label 83.33%. it is produces high equity group composed of 11 districts with equity groups are composed of 15 districts and low equity group consists of 46 sub-districts. Keywords—Indicator of Educational Equity, Clustering, Partition Around Medoid, Distance Measure, Adjusted Random Index .

scholarly journals An Improved WiFi Positioning Method Based on Fingerprint Clustering and Signal Weighted Euclidean Distance

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2300 ◽  
Author(s):  
Boyuan Wang ◽  
Xuelin Liu ◽  
Baoguo Yu ◽  
Ruicai Jia ◽  
Xingli Gan
Keyword(s):  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Physical Distance ◽  
Reference Points ◽  
Manhattan Distance ◽  
Position Distribution ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Positioning Method

WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.

A Comprehensive Study on Intelligent Transportation Systems

2018 ◽  
Vol 4 (10) ◽  
pp. 10
Author(s):  
Ankur Mishra ◽  
Aayushi Priya
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Transportation Systems ◽  
Environmental Conservation ◽  
Integrated System ◽  
Transport Systems ◽  
Transport Sector ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Transportation Sector

Transportation or transport sector is a legal source to take or carry things from one place to another. With the passage of time, transportation faces many issues like high accidents rate, traffic congestion, traffic & carbon emissions air pollution, etc. In some cases, transportation sector faced alleviating the brutality of crash related injuries in accident. Due to such complexity, researchers integrate virtual technologies with transportation which known as Intelligent Transport System. Intelligent Transport Systems (ITS) provide transport solutions by utilizing state-of-the-art information and telecommunications technologies. It is an integrated system of people, roads and vehicles, designed to significantly contribute to improve road safety, efficiency and comfort, as well as environmental conservation through realization of smoother traffic by relieving traffic congestion. This paper aims to elucidate various aspects of ITS - it's need, the various user applications, technologies utilized and concludes by emphasizing the case study of IBM ITS.

scholarly journals Moroccan Video Intelligent Transport System: Vehicle Type Classification Based on Three-Dimensional and Two-Dimensional Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 72528-72537 ◽  
Author(s):  
Hatim Derrouz ◽  
Abderrahim Elbouziady ◽  
Hamd Ait Abdelali ◽  
Rachid Oulad Haj Thami ◽  
Sanaa El Fkihi ◽  
...  
Keyword(s):  
Transport System ◽  
Three Dimensional ◽  
Intelligent Transport System ◽  
Two Dimensional ◽  
Intelligent Transport ◽  
Vehicle Type ◽  
Vehicle Type Classification ◽  
Type Classification

An approach for document retrieval using cluster-based inverted indexing

2021 ◽  
pp. 016555152110184
Author(s):  
Gunjan Chandwani ◽  
Anil Ahlawat ◽  
Gaurav Dubey
Keyword(s):  
High Performance ◽  
Clustering Algorithm ◽  
Pearson Correlation ◽  
Relevant Information ◽  
Document Retrieval ◽  
Bhattacharyya Distance ◽  
Data Set ◽  
Query Matching ◽  
Inverted Indexing ◽  
Query Optimisation

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

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