3,945 research outputs found
A survey on utilization of data mining approaches for dermatological (skin) diseases prediction
Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data
Subnetwork Constraints for Tighter Upper Bounds and Exact Solution of the Clique Partitioning Problem
We consider a variant of the clustering problem for a complete weighted
graph. The aim is to partition the nodes into clusters maximizing the sum of
the edge weights within the clusters. This problem is known as the clique
partitioning problem, being NP-hard in the general case of having edge weights
of different signs. We propose a new method of estimating an upper bound of the
objective function that we combine with the classical branch-and-bound
technique to find the exact solution. We evaluate our approach on a broad range
of random graphs and real-world networks. The proposed approach provided
tighter upper bounds and achieved significant convergence speed improvements
compared to known alternative methods.Comment: 20 pages, 3 figure
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