Yazar "Bilgin, T. T." seçeneğine göre listele

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    Applied Comparison of DBSCAN, OPTICS and K-Means Clustering Algorithms
    (Gazi Univ, 2005) Bilgin, T. T.; Camurcu, Yılmaz
    DBSCAN and OPTICS are two recent clustering algorithms on data mining. In this study, these two algorithms and K-means which is one of the oldest clustering algorithms are compared. Comparison is based on cluster discovery performance on synthetic database. Consequently, two recent clustering algorithms DBSCAN and OPTICS are performed superior accuracy and cluster discovery ability over K-means algorithm.
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    A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data
    (INST ENGINEERING TECHNOLOGY-IET, 2012) Erdogan, S. Z.; Bilgin, T. T.
    Fall detection technology is critical for the elderly people. In order to avoid the need of full time care giving service, the actual trend is to encourage elderly to stay living autonomously in their homes as long as possible. Reliable fall detection methods can enhance life safety of the elderly and boost their confidence by immediately alerting fall cases to caregivers. This study presents an algorithm of fall detection, which detects fall events by using data-mining approach. The authors' proposed method performs detection in two steps. First, it collects the wireless sensor network (WSN) data in stream format from sensor devices. Second, it uses k-nearest neighbour algorithm, that is, well-known lazy learning algorithm to detect fall occurrences. It detects falls by identifying the fall patterns in the data stream. Experiments show that the proposed method has promising results on WSN data stream in detecting falls.