دانلود مقاله ISI انگلیسی شماره 79110
عنوان فارسی مقاله

یک الگوریتم خوشه بندی مبتنی بر تراکم کارآمد و مقیاس پذیر برای مجموعه داده های با ساختارهای پیچیده

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79110 2016 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
An efficient and scalable density-based clustering algorithm for datasets with complex structures
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Neurocomputing, Volume 171, 1 January 2016, Pages 9–22

کلمات کلیدی
خوشه بندی مبتنی بر تراکم - محل هش حساس؛ فضای نفوذ؛ تشخیص اشیاء مرز
پیش نمایش مقاله
پیش نمایش مقاله یک الگوریتم خوشه بندی مبتنی بر تراکم کارآمد و مقیاس پذیر برای مجموعه داده های با ساختارهای پیچیده

چکیده انگلیسی

As a research branch of data mining, clustering, as an unsupervised learning scheme, focuses on assigning objects in the dataset into several groups, called clusters, without any prior knowledge. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most widely used clustering algorithms for spatial datasets, which can detect any shapes of clusters and can automatically identify noise points. However, there are several troublesome limitations of DBSCAN: (1) the performance of the algorithm depends on two specified parameters, ε and MinPts in which ε represents the maximum radius of a neighborhood from the observing point and MinPts means the minimum number of data points contained in such a neighborhood. (2) The time consumption for searching the nearest neighbors of each object is intolerable in the cluster expansion. (3) Selecting different starting points results in quite different consequences. (4) DBSCAN is unable to identify adjacent clusters of various densities. In addition to these restrictions about DBSCAN mentioned above, the identification of border points is often ignored. In our paper, we successfully solve the above problems. Firstly, we improve the traditional locality sensitive hashing method to implement fast query of nearest neighbors. Secondly, several definitions are redefined on the basis of the influence space of each object, which takes the nearest neighbors and the reverse nearest neighbors into account. The influence space is proved to be sensitive to local density changes to successfully reduce the amount of parameters and identify adjacent clusters of different densities. Moreover, this new relationship based on the influence space makes the insensitivity to the ordering of inputting points possible. Finally, a new concept—core density reachable based on the influence space is put forward which aims to distinguish between border objects and noisy objects. Several experiments are performed which demonstrate that the performance of our proposed algorithm is better than the traditional DBSCAN algorithm and the improved algorithm IS-DBSCAN.

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