Focusing on multi-document personal name disambiguation, this paper develops an agglomerative clustering approach to resolving this problem. We start from an analysis of pointwise mutual information between feature and the ambiguous name, which brings about a novel weight computing method for feature in clustering. Then a trade-off measure between within-cluster compactness and among-cluster se...

This paper presents a supervised variable selection method applied to regression problems. This method selects the variables applying a hierarchical clustering strategy based on information measures. The proposed technique can be applied to single-output regression datasets, and it is extendable to multi-output datasets. For single-output datasets, the method is compared against three other var...

In this paper, we propose an effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering), to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the conve...

The functional Magnetic Resonance Imaging (fMRI) has provided us with an approach of revealing the activity of brain. Due to the large amount of data in fMRI studies, feature selection techniques are used to select particular features for classifier. In this project, Spectral Clustering is implemented to construct features to achieve best reconstruction of the data and be most efficient for mak...

To overcome the absence of concrete supervisory signals, deep clustering models construct their own labels based on self-supervision and pseudo-supervision. However, applying these techniques can cause Feature Randomness Drift. In this paper, we formally characterize two new concepts. On one hand, takes place when a considerable portion pseudo-labels is deemed to be random. regard, trained mode...

Current approaches to improve the pattern recognition performance mainly focus on either extracting non-stationary and discriminant features of each class, or employing complex and nonlinear feature classifiers. However, little attention has been paid to the integration of these two approaches. Combining non-stationary feature analysis with complex feature classifiers, this article presents a n...

Remote sensing plays a vital role in overseeing the transformations on the earth surface. Unsupervised clustering has a indispensable role in an immense range of applications like remote sensing, motion detection, environmental monitoring, medical diagnosis, damage assessment, agricultural surveys, surveillance etc In this paper, a novel method for unsupervised classification in multitemporal o...

We commend Jin and Wang on a very interesting paper introducing a novel approach to feature selection within clustering and a detailed analysis of its clustering performance under a Gaussian mixture model. I shall divide my discussion into several parts: (i) prior work on feature selection and clustering; (ii) theoretical aspects; (iii) practical aspects; and finally (iv) some questions and dir...

Feature selection has improved the performance of text clustering. In this paper, a local feature selection technique is incorporated in the dynamic hierarchical compact clustering algorithm to speed up the computation of similarities. We also present a quality measure to evaluate hierarchical clustering that considers the cost of finding the optimal cluster from the root. The experimental resu...