The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Cluster ensemble is one of the main branches in the ensemble learning area which is an important research focus in recent years. The objective of cluster ensemble is to combine multiple clustering solutions in a suitable way to improve the quality of the clustering result. In this paper, we design a new noise immune cluster ensemble framework named as $AP^{2}CE$<alternatives> <inline-graphic xlink:type="simple" xlink:href="yu-ieq1-2453162.gif"/></alternatives>...
It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This paper proposes an efficient algorithm used to evaluate the results of cluster analysis in classifying the patients with the tumor. The Prediction Strength, combined with...
Computer vision-based diagnosis systems have been widely used in dermatology, aiming at the early detection of skin cancer and more specifically the recognition of malignant melanoma tumor. This paper proposes a novel clustering technique for the characterization and categorization of pigmented skin lesions in dermatological images. Appropriate image processing techniques (i.e. segmentation, border...
Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attributes. The goal is to equalize the size or magnitude and the variability of these features. This can also be seen as a way to adjust the relative weighting of the attributes. In this context, we present a first large scale data...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.