In this empirical study, our goal is to investigate the effectiveness of clustering high dimensional data using principle components (PCs) instead of original variables. Effects of PCs instead original variables on cl...
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In this empirical study, our goal is to investigate the effectiveness of clustering high dimensional data using principle components (PCs) instead of original variables. Effects of PCs instead original variables on clustering of simulated data sets which have different features are investigated by two different criteria. Moreover in this study we also showed that the effectiveness of clustering high dimensional data using standardized variables instead of original variables.
A case study applying the generalized k-harmonic meansalgorithm is provided to select the applicants under a variety of criteria in an admission process. The properties of the generalized k-harmonic meansalgorithm, ...
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A case study applying the generalized k-harmonic meansalgorithm is provided to select the applicants under a variety of criteria in an admission process. The properties of the generalized k-harmonic meansalgorithm, including the extreme case, are exploited to examine the performance in a decision-making process. The results based upon this case study show that the generalized k-harmonic meansalgorithm can become a very powerful technique in not only prioritizing the alternatives but also forming the similarities of alternatives in a decision-making process with appropriate use.
The distraction affects driving performance and induces serious safety issues. To better understand distracted driving, this study examines the influence of distracted driving on overall driving performance. This pape...
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The distraction affects driving performance and induces serious safety issues. To better understand distracted driving, this study examines the influence of distracted driving on overall driving performance. This paper analyzes the distraction behavior (mobile phone use, entertainment activities, and passenger interference) under three driving tasks. The statistical results show that viewing or sending messages is common during driving. Smoking, phone calls, and talking to passengers are evident in cruising, ride request and drop-off, respectively. Then, overall driving performance is proposed based on velocity, longitudinal acceleration (longacc) and yaw_rate. It is divided into three categories, high, medium, and low, by k-means algorithms. The average speed increases from low to high performance; however, the longacc and yaw_rate decrease. Finally, the influence of distracted driving on overall driving performance is analyzed using C4.5 algorithm. The result shows that when time is peak, the probability of high performance (HP) is higher than off-peak. The possibility of HP increases with the increase of duration; the number of, talking to passengers, listening to music or radio, eating; the duration of, viewing or sending messages, phone calls; but reduces with the increase of the number of phone calls. These findings provide theoretical support for driving performance evaluation.
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