Slide 1IntroductionRecall of My Senior Design problemRecall of My Senior Design problem (continue)Article proposed AlgorithmsArticle proposed Algorithms (continue)Slide 7How would these algorithms may help my teamQuestions?Algorithms featured by:Jong Hee KangWilliam WelbourneDenjamin StewartGaetano DorrielloPresent by:Yuan LiuIntroductionWhat are these algorithms for?Extracting locations from cluster pointsWhat are these algorithms trying to achieve?Significantly reduce the server loadMaintain data accuracyTranslate coordinates to locationsRecall of My Senior Design problemRecall of My Senior Design problem(continue)What did our customer wants:Plug collected GPS points into Google MapCan group these points manually or automaticallyCustomize these grouped points (name, etc)Our Approach:Use Area grouping (draws a circle)Use a automatic filterArticle proposed AlgorithmsTime based Clustering:clustering locations along the time axisNew location is compared with previous locationsIf the new location is moving away starts a new cluster Then ignore the clusters with short time durationArticle proposed Algorithms(continue)Article proposed Algorithms(continue)Frequency clustering:Time based Clustering algorithm only consists data with longer time durationPeople may visit important place frequently, but not for a long time (such as ATM, mailbox)Need two threshold value: one determines duration, the other determines the frequency of visiting.May yield false information (stop at traffic light)How would these algorithms may help my teamWheelchair people usually does not move around in a big radius, and they tend to stay at one place for a long period of time, therefore the Time clustering algorithms applies the best.The server we were using is quite weak, and we didn’t assume everyone has broadband. Such algorithms will greatly reduce the calculation
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