Comparative Analysis of Clustering Algorithms Based on Driver Steering Characteristics

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FCM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFCMwUNC clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FCM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFCMwUNC clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFCMwUNC clustering algorithm for the symbolic interval-values data.

Clustering algorithms aim to detect groups based on similarity, from a given set of objects. Many clustering techniques have been proposed, most requiring the user to set critical parameters, such as the number of groups. This work presents the implementation and evaluation of a clustering algorithm based on a multi-agent system, which automatically detects the number of groups and the group labels for a given dataset. Groups formed during the clustering process emerge as patterns from the interaction among agents. The proposed algorithm is experimentally validated over benchmark datasets from the literature. The quality of clustering results is computed using seven internal indexes and one external index. Under this methodology, the proposed algorithm is compared to K-means and DBSCAN (density-based spatial clustering of applications with noise).

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.

<div class="htmlview paragraph"> </div> <div class="htmlview paragraph"> </div> <div class="htmlview paragraph">The quality of exhaust sound has become one of the important factors in the motorcycle market.</div> <div class="htmlview paragraph"> </div> <div class="htmlview paragraph">Therefore both an efficient sound quality evaluation method and technology to achieve ideal sound quality have become necessary.</div> <div class="htmlview paragraph"> </div> <div class="htmlview paragraph">Sound qualify evaluation has generally been performed by trial and error through repeated modification of exhaust silencer construction until desired quality was obtained.</div> <div class="htmlview paragraph"> </div> <div class="htmlview paragraph">But it usually took painstaking work and long hours.</div> <div class="htmlview paragraph"> </div> <div class="htmlview paragraph">In order to solve such problems, we established an objective auditory evaluation method.</div> <div class="htmlview paragraph"> </div> <div class="htmlview paragraph">We also applied Principal component analysis to analize the result of the “Semantic Differential Method (SD method)” so as to determine the affecting elements.</div> <div class="htmlview paragraph"> </div> <div class="htmlview paragraph">Through this analysis system, “powerful sound” caused by relatively higher content of the low frequency range and “crispy sound” caused by a cyclic sound pattern were determined to be desirable sounds for “American type” motorcycles.</div> <div class="htmlview paragraph"> </div> <div class="htmlview paragraph">We established a system for approximate frequency distribution characteristic estimation for simply constructed exhaust silencers by introducing a numerical computation method, called “Finite Element Method (FEM)” and “Boundary Element Method(BEM)” to estimate the sound when exhaust silencer component elements are modified.</div> <div class="htmlview paragraph"> </div>

<p>Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a division strategy (KDG) is proposed to divide the cell of grid adaptively. Secondly, an improve fruit fly optimization algorithm (IFOA) which use the step strategy based on knowledge learn (KLSS) and the clustering criterion function (CFF) is designed. In addition, based on IFOA algorithm, the optimal parameters of local clustering are dynamically selected, which can improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, the density-based clustering algorithm using IFOA (MR-QRMEC) are proposed to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce, a cluster merging algorithm (MR-QRMEC) is proposed to get the result of clustering algorithm more quickly, which improve the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA algorithm has better clustering results and performs better parallelization in big data.</p> <p> </p>

<div class="htmlview paragraph">Information on the driving stability can be obtained in the form of oversteer or understeer tendencies and acceleration limits on a circular path. Up to now the calculations have only been made on an ideal, even roadway. Significant dynamic characteristics, such as the tuning of the shock absorbers, which also play a role in evaluating the stability of a vehicle in a driving test, are thereby not included in the calculations.</div> <div class="htmlview paragraph">In this paper the unevenness of the roadway is taken into consideration in calculating steady state cornering tests. Different vehicle models can be evaluated in regard to their handling or active safety with the aid of the driver's steering actions required to keep the car on course.</div>

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.

In this paper, the method that measuring dataset of knitted yarns is clustered using improving fuzzy kernel c-Means (FKCM) clustering algorithm is proposed. In FKCM clustering algorithm, the data of low dimension input space is mapped to high dimension feature space, FCM clustering algorithm is performed in feature space, then the constraint optimization distance matrix and membership matrix of testing samples are computed by utilizing iterative algorithm, and the clustering result can be acquired according maximum membership principle. Subsequently, the Kernel F cluster validity index is designed for seeking the fitness cluster number and the corresponding relationship model of clusters sequence number and quality grades is constructed. Improving FKCM clustering algorithm is more efficient than other clustering algorithms, and clustering result can provide the training samples for constructing quality grades and clusters recognition function of new samples. The combination of improving FKCM and KF index provides an efficient data analysis method for multi-index dataset.

Use of a fuzzy granulation–degranulation criterion for assessing cluster validity

<div class="section abstract"><div class="htmlview paragraph"><b>Background</b>: The Indian automobile industry, including the auto component industry, is a significant part of the country’s economy and has experienced growth over the years. India is now the world’s 3<sup>rd</sup> largest passenger car market and the world’s second-largest two-wheeler market. Along with the boon, the bane of road accident fatalities is also a reality that needs urgent attention, as per a study titled ‘Estimation of Socio-Economic Loss due to Road Traffic Accidents in India’, the socio-economic loss due to road accidents is estimated to be around 0.55% to 1.35% of India’s GDP [<span class="xref">27</span>] Ministry of road transport and highways (MoRTH) accident data shows that the total number of fatalities on the road are the highest (in number terms) in the world. Though passenger car occupant fatalities have decreased over the years, the fatalities of vulnerable road users are showing an increasing trend. India has committed to reduce road fatalities by 50% by 2030. In this context, the automotive industry as well as MoRTH have been taking multiple initiatives including those related to vehicular and road engineering as well as educational measures for raising awareness in the field of road safety.</div><div class="htmlview paragraph">With the intent of lowering road traffic accidents, the government and the ministry are constantly upgrading the requirements related to vehicles, infrastructures, and their implementation thereof. In this context, various future regulations are being deliberated. One such regulation in discussion pertains to requirements for the protection of the passenger car occupants in the event of a full-frontal collision with a focus on restraint systems (AIS 201). This will push manufacturers to design more sophisticated restraints that would help to reduce restraint-induced injuries. The draft version of the standard is available for public input/ discussion.</div><div class="htmlview paragraph">The key objective of the study documented herein is to consider various aspects of the draft standard (AIS 201) from the Indian perspective. This study investigates the research questions, enumerated below: <ol class="list nostyle"><li class="list-item"><span class="li-label">1</span><div class="htmlview paragraph">What should be the appropriate test speed for the full-frontal test based on Indian accident data?</div></li><li class="list-item"><span class="li-label">2</span><div class="htmlview paragraph">What is the suitable dummy configuration in terms of gender, seating position, and age to maximize occupant safety in full frontal accidents?</div></li><li class="list-item"><span class="li-label">3</span><div class="htmlview paragraph">Is the proposed ATD’s anthropometry (weight and height) suitable, based on the people involved in full frontal cases in India?</div></li><li class="list-item"><span class="li-label">4</span><div class="htmlview paragraph">What are occupant injury attributes in full-frontal accidents?</div></li></ol></div><div class="htmlview paragraph"><b>Key findings/ expected research findings</b>: India has a very different mix of road traffic users and road traffic fatalities compared to that in Europe (Refer to <span class="xref">Table 1</span>). Further, issues like seat belt usage are of utmost concern. The demographic for India is also very different from those of developed economies. With India being a country with a rather young population, male and female dummies for driver and front passenger positions should be looked upon. This calls for India-specific safety regulations and research work on automotive test dummies.</div><div class="htmlview paragraph"><b>Practical implications</b>: The study can potentially provide a suggestion for alignment of the regulatory requirements with the actual situations, thereby increasing the effectiveness of the standard.</div></div>

As big data technology penetrates all aspects of our lives; more and more industries are beginning to apply big data technology. Its robust data analysis, resource acquisition, and predictive capabilities have brought tremendous business activities as a brand-new data management technology. This article analyzes the big data technology application in business activities and introduces the principles of two essential algorithms in big data technology, clustering algorithm, and principal component analysis. Moreover, it introduces applying the mean shift clustering and principal component analysis in clustering algorithm to analyze the commercial data. Finally, a comprehensive analysis of the advantages and disadvantages of big data technology is made.

<div class="htmlview paragraph">The authors have developed a steering entropy method to easily and accurately quantify the workload imposed on drivers who are engaged in activities apart from the normal driving operations of longitudinal and lateral control. A driver's steering behavior tends to become more discontinuous while performing an activity in addition to driving. To quantify these discontinuities, steering entropy values are obtained from a time-series history of steering angle data. A special-purpose driving simulator and a test procedure have been developed that allow workload evaluations to be conducted efficiently. The simulator and test procedure were used to evaluate the additional workload incurred by 14 different types of activities. The steering entropy results were compared with a dual task method as well as a subjective evaluation method. These results showed a high degree of correlation between the three approaches, which confirmed the validity of the steering entropy method as a means to quantify workload. Measurements were then made of progressively more difficult tasks. It was found that the steering entropy values increased in direct proportion to the degree of (subjective) task difficulty, which corroborated the high measurement accuracy of this method.</div>

This article applies principal component analysis (PCA) and cluster analysis to solve the customer classification problem of an actual power supply network. First, the article defines the index system which will influence the cost of power supply significantly. The index system includes power supply, maximum need, load rate etc. Second, the article uses PCA to filter the source data from these index items into several main components. Third, these main components will be analyzed with system clustering algorithm and k-means clustering algorithm to make clustering analysis and comparison and provide the calculation method about how to make the efficiency test of these clustering results. The clustering results prove that the source data can be well classified with PCA method with the new indexes reflecting the characteristics of the whole source data. At last the article also proves the similarity of the results obtained from system clustering and K-means clustering separately and verifies the two clustering algorithm all can be used to actual analysis. The multivariate statistical analysis proposed in this article can reflect the characteristic of customer's electrical behavior can be more objective and comprehensive that traditional methods and can a good tool for power price decision maker.

Analyzing the electricity price plays a vital role in market players in deregulated electricity markets. In this regard, proper clustering methods are more beneficial. In this paper, a comparative study of three major clustering algorithms, including K-Means, Fuzzy C-Means, and Hierarchical algorithm on the Italian National Single Price, has been carried out. Moreover, the impact of various parameters of the algorithms on the clustering results has been analyzed. The performance of the clustering methods has been compared through several clustering validity indexes, including the Silhouette index, Calinski-Harabasz index, and Davies-Bouldin indicator. Two distinct patterns consisting of working days and weekends (or holidays) are observable in the particular dataset.