A Novel Approach for Traffic Accidents Analysis Using Hierarchical Clustering Techniques

2021 ◽  
Author(s):  
Kinnari Parikh ◽  
Dr. Gayatrri Pandi
Keyword(s):  
Hierarchical Clustering ◽  
Traffic Accidents ◽  
Clustering Techniques ◽  
Novel Approach

Web Data Extraction with Hierarchical Clustering and Rich Features

2011 ◽  
Vol 55-57 ◽  
pp. 1003-1008
Author(s):  
Yong Quan Dong ◽  
Xiang Jun Zhao ◽  
Gong Jie Zhang
Keyword(s):  
Hierarchical Clustering ◽  
Data Extraction ◽  
Web Pages ◽  
Web Data ◽  
Clustering Techniques ◽  
Feature Vectors ◽  
Web Data Extraction ◽  
Novel Approach ◽  
Data Elements ◽  
Content Feature

A novel approach is proposed to automatically extract data records from detail pages using hierarchical clustering techniques. The approach uses the information of the listing pages to identify the content blocks in detail pages, which narrows the scopes of Web data extraction. Meanwhile, it also makes full use of the structure and content features to cluster content feature vectors. Finally, it aligns data elements of multiple details pages to extract the data records. Experiment results on test beds of real web pages show that the approach can achieve high extraction accuracy and outperforms the existing techniques substantially.

scholarly journals Identifying and Labeling Potentially Risky Driving: A Multistage Process Using Real-World Driving Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Charles Marks ◽  
Arash Jahangiri ◽  
Sahar Ghanipoor Machiani
Keyword(s):  
Real World ◽  
Traffic Accidents ◽  
Network Models ◽  
Multistage Process ◽  
Neural Network Models ◽  
Risky Driving ◽  
Vehicle Accidents ◽  
Driving Behaviors ◽  
Novel Approach ◽  
Risky Driving Behaviors

Every year, over 50 million people are injured and 1.35 million die in traffic accidents. Risky driving behaviors are responsible for over half of all fatal vehicle accidents. Identifying risky driving behaviors within real-world driving (RWD) datasets is a promising avenue to reduce the mortality burden associated with these unsafe behaviors, but numerous technical hurdles must be overcome to do so. Herein, we describe the implementation of a multistage process for classifying unlabeled RWD data as potentially risky or not. In the first stage, data are reformatted and reduced in preparation for classification. In the second stage, subsets of the reformatted data are labeled as potentially risky (or not) using the Iterative-DBSCAN method. In the third stage, the labeled subsets are then used to fit random forest (RF) classification models—RF models were chosen after they were found to be performing better than logistic regression and artificial neural network models. In the final stage, the RF models are used predictively to label the remaining RWD data as potentially risky (or not). The implementation of each stage is described and analyzed for the classification of RWD data from vehicles on public roads in Ann Arbor, Michigan. Overall, we identified 22.7 million observations of potentially risky driving out of 268.2 million observations. This study provides a novel approach for identifying potentially risky driving behaviors within RWD datasets. As such, this study represents an important step in the implementation of protocols designed to address and prevent the harms associated with risky driving.

CLUSTERING OF DECISION TABLES TOWARD ROUGH SET-BASED GROUP DECISION AID

2011 ◽  
Vol 19 (supp01) ◽  
pp. 17-32 ◽  
Author(s):  
MASAHIRO INUIGUCHI ◽  
RYUTA ENOMOTO
Keyword(s):  
Hierarchical Clustering ◽  
Rough Set ◽  
Real World ◽  
Decision Aid ◽  
Group Decision ◽  
Decision Rules ◽  
Agglomerative Hierarchical Clustering ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Decision Tables

In order to analyze the distribution of individual opinions (decision rules) in a group, clustering of decision tables is proposed. An agglomerative hierarchical clustering (AHC) of decision tables has been examined. The result of AHC does not always optimize some criterion. We develop non-hierarchical clustering techniques for decision tables. In order to treat positive and negative evaluations to a common profile, we use a vector of rough membership values to represent individual opinion to a profile. Using rough membership values, we develop a K -means method as well as fuzzy c-means methods for clustering decision tables. We examined the proposed methods in clustering real world decision tables obtained by a questionnaire investigation.

scholarly journals NON-TRIVIAL FEATURE DERIVATION FOR INTENSIFYING FEATURE DETECTION USING LIDAR DATASETS THROUGH ALLOMETRIC AGGREGATION DATA ANALYSIS APPLYING DIFFUSED HIERARCHICAL CLUSTERING FOR DISCRIMINATING AGRICULTURAL LAND COVER IN PORTIONS OF NORTHERN MINDANAO, PHILIPPINES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1009-1016
Author(s):  
Ricardo G. Villar ◽  
Jigg L. Pelayo ◽  
Ray Mari N. Mozo ◽  
James B. Salig Jr. ◽  
Jojemar Bantugan
Keyword(s):  
Data Analysis ◽  
Hierarchical Clustering ◽  
Feature Detection ◽  
Agricultural Land ◽  
Feature Space ◽  
Machine Learning Algorithms ◽  
Kappa Index ◽  
Cloud Data ◽  
Allometric Relations ◽  
Novel Approach

Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m<sup>2</sup> of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this investigation was compared to a classification based on conventional object-oriented approach promoting straight-forward functionalities of the software eCognition. A compelling rise of efficiency in the overall accuracy (74.4% to 93.4%) and kappa index of agreement (70.5% to 91.7%) is noticeable based on the initial process. Nevertheless, having low-dense LiDAR dataset could be enough in generating exponential increase of performance in accuracy.

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