https://padlet.com/transfinity/9gi3qiflqxw7 สร้างด้วยเวทย์มนตร์ en-us 2017-02-10 09:03:46 UTC 2017-02-10 09:31:42 UTC hello@padlet.com https://padlet.com/transfinity/9gi3qiflqxw7/wish/152997128  Clustering plays a vital role in the various areas of research like Data Mining, Image Retrieval, Bio-computing and many a lot. Distance measure plays an important role in clustering data points. Choosing the right distance measure for a given dataset is a biggest challenge. In this paper, we study various distance measures and their effect on different clustering. This paper surveys existing distance measures for clustering and present a comparison between them based on application domain, efficiency, benefits and drawbacks. This comparison helps the researchers to take quick decision about which distance measure to use for clustering. We conclude this work by identifying trends and challenges of research and development towards clustering. ]]> 2017-02-10 09:10:07 UTC https://padlet.com/transfinity/9gi3qiflqxw7/wish/152997128 https://padlet.com/transfinity/9gi3qiflqxw7/wish/152997175 We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called “dropout” that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012competitionandachievedawinningtop-5testerrorrateof15.3%, compared to 26.2% achieved by the second-best entry]]> 2017-02-10 09:10:27 UTC https://padlet.com/transfinity/9gi3qiflqxw7/wish/152997175 https://padlet.com/transfinity/9gi3qiflqxw7/wish/152997368  Abstract. Micro-blogs are a challenging new source of information for data mining techniques. Twitter is a micro-blogging service built to discover what is happening at any moment in time, anywhere in the world. Twitter messages are short, and generated constantly, and well suited for knowledge discovery using data stream mining. We briefly discuss the challenges that Twitter data streams pose, focusing on classification problems, and then consider these streams for opinion mining and sentiment analysis. To deal with streaming unbalanced classes,  we propose a sliding window Kappa statistic for evaluation in time-changing data streams. Using this statistic we perform a study on Twitter data using learning algorithms for data streams. ]]> 2017-02-10 09:11:40 UTC https://padlet.com/transfinity/9gi3qiflqxw7/wish/152997368