Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

Emotion and Adaptation

In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover's Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.

/pdf/emotion-computing-using-word-mover-s-distance-features-based-1xvilp143p.pdf

Emotion computing using Word Mover's Distance features based on Ren_CECps.

In this work we presented a new parameter-free thresholding method for image segmentation. In separating an image into two classes, the method employs an objective function that not only maximizes the between-class variance but also the distance between the mean of each class and the global mean of the image. The design of the objective function aims to circumvent the challenge that many existing techniques encounter when the underlying two classes have very different sizes or variances. Advantages of the new method are two-fold. First, it is parameter-free, meaning that it can generate consistent results. Second, the new method has a simple form that makes it easy to adapt to different applications. We tested and compared the new method with the standard Otsu method, the maximum entropy method, and the 2D Otsu method on simulated and real biomedical and photographic images and found the new method can achieve a more accurate and robust performance.

/pdf/a-robust-parameter-free-thresholding-method-for-image-53k62pumiu.pdf

A Robust Parameter-free Thresholding Method for Image Segmentation.

Domain knowledge can be introduced into fuzzy clustering with the aid of information granules, embodied by the concept of viewpoints. For such kind of fuzzy clustering methods, the strategy of acquisition of viewpoints has not been fully developed. Furthermore a way of determining the related information granules deserves more attention. Having these problems in mind, in this study, the density Viewpoint-based Weighted Kernel Fuzzy Clustering (VWKFC) algorithm is proposed. First, the kernel-based hypersphere density initialization (KHDI) algorithm is presented as a certain prerequisite, in which the kernel distance is utilized instead of the Euclidean distance. Besides, a novel density radius is put forward. Second, the concept of the weight information granule is established, which incorporates two parts. The feature weight matrix is provided, where different weights are assigned to different features to reduce the influence of unrelated features. Meanwhile a sample weight is assigned to each data point, thus the influence of noise and outliers on clustering can be reduced to a certain extent. Third, the data point with the highest local density obtained by KHDI is regarded as the density viewpoint. Then we combine kernel mechanism, density viewpoints, weight information granules and a maximum entropy regularization to design the VWKFC algorithm, and prove its convergence. Experimental results validate that VWKFC is superior over eight related clustering algorithms with regard to five evaluation indexes, especially when processing high-dimensional data. It has been shown that VWKFC makes the selection of initialized cluster centers and viewpoints more reasonable, and obtains better clustering results, and achieves higher convergence speed.

Viewpoint-Based Kernel Fuzzy Clustering With Weight Information Granules

Recently, there is a lot of research that aims to estimate emotion from text. The meanings of linguistic expressions used in daily life vary depending on the context in which they are used. That is to say, the information they contain presents ambiguities. Especially the so-called “Wakamono Kotoba,” Japanese language used by young people contains semantic ambiguities. Such words are usually not included in the existing dictionaries, making the meanings of these words difficult to be recognized. In this research project we proposed a method to estimate emotion from sentences that include Wakamono Kotoba by using statistical learning methods such as Naive Bayes method and Accumulation method. The existing research usually focused on learning methods using word or word N-gram as features. However, such word-based features are insufficient to process Wakamono Kotoba because Wakamono Kotoba often cannot be recognized as one semantic word by morphological analysis. In this paper we describe how we constructed the linguistic resource of Wakamono Kotoba emotion corpus to be used for emotion recognition and introduce the features we obtained from statistical analysis. Our Wakamono Kotoba emotion corpus includes Japanese words used by young people to express emotion. These were mainly gathered from Weblogs that were written by young people from their teens to their twenties.

Analysis of Wakamono Kotoba Emotion Corpus and Its Application in Emotion Estimation

The rapid growth of the Internet has resulted in enormous amounts of information that has become more difficult to access efficiently. The primary goal of this research is to create an efficient tool that is able to summarize large documents automatically. We propose concept chains to link semantically-related concepts based on Hownet knowledge database to improve the performance of text summarization and suit Chinese text. Lexical chains is a technique for identifying semantically-related terms in text. The resulting concept chains are then used to identify candidate sentences useful for extraction. Moreover, the other method based on structural features which can makes the summary of the text have more general content and more balance is also proposed. The final experimental results proved the effectiveness of our methods.

Fuji Ren

Papers

Emotion computing using Word Mover's Distance features based on Ren_CECps.

A Robust Parameter-free Thresholding Method for Image Segmentation.

Viewpoint-Based Kernel Fuzzy Clustering With Weight Information Granules

Analysis of Wakamono Kotoba Emotion Corpus and Its Application in Emotion Estimation

Automatic Text Summarization Based on Lexical Chains and Structural Features