Multi-label learning is a challenge problem in computer vision fields. Since annotating a multilabel instance costs greatly, multi-label classification has become a hot topic research. State-of-theart active learning methods either annotate all the relevant samples without diagnosing discriminative information in the labels or annotate only limited discriminative samples manually, that has weak...

ABSTRACT-In Data mining there are lots of methods are used to detect the outlier by making the clusters of data and then detect the outlier from them. In general Clustering method plays a very important role in data mining. Clustering means grouping the similar data objects together based on the characteristic they possess. Outlier Detection is an important issue in Data mining; particularly it...

Uncertain data management, querying and mining have become important because the majority of real world data is accompanied with uncertainty these days. Uncertainty in data is often caused by the deficiency in underlying data collecting equipments or sometimes manually introduced to preserve data privacy. The uncertainty information in the data is useful and can be used to improve the quality o...

Protein kinases represent the most effective class of therapeutic targets in cancer; therefore, determination of kinase aberrations is a major focus of cancer genomic studies. Here, we analyzed transcriptome sequencing data from a compendium of 482 cancer and benign samples from 25 different tissue types, and defined distinct "outlier kinases" in individual breast and pancreatic cancer samples,...

Detecting outliers which are grossly different from or inconsistent with the remaining spatio-temporal dataset is a major challenge in real-world knowledge discovery and data mining applications. In this paper, we deal with the outlier detection problem in spatio-temporal data and we describe a rough set approach that finds the top outliers in an unlabeled spatio-temporal dataset. The proposed ...

For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commerce. Being an outlier, however, is not just a binary property. Instead, it is a property that applies to a certain degree to each object in a data set, depending on how ‘isolated’ this object is, with respect to the surr...

Successfully detecting outliers in multivariate data requires statistical and programming skills and can be very time consuming. Requests for outlier detection can come from different skills groups therefore it is more efficient and effective to allow users to interact directly with the data themselves. We have developed an interactive, web based data visualization application for outlier detec...

Outlier detection is a primary step in many data-mining applications. We present several methods for outlier detection, while distinguishing between univariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for data mining is often based on dist...

Image alignment is one of the most widely used techniques in computer vision. Applications range from optical flow and tracking to layered motion, mosaic-ing, and face coding. Of particular concern in many applications is the efficiency of the algorithm. This concern has led to the development of several efficient algorithms such as the Hager-Belhumeur algorithm and the inverse compositional al...