One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...

When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy an instance often depends on not only itself but also its context in corresponding bag. From viewpoint causal inference, such bag contextual prior works as a confounder and may result model robustness interpretability issues. Focusing this problem, we propose novel interventional (IMIL...

Clustering is one of most important methods of data mining. It is used to identify unknown yet interesting and useful patterns or trends in datasets. There are different types of clustering algorithms such as partitioning, hierarchical, grid and density-based. In general, clustering methods are considered unsupervised, however, in recent years the new branch of clustering algorithms has emerged...

Clustering techniques often define the similarity between instances using distance measures over the various dimensions of the data [12, 14]. Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. Traditional clustering algorithms consider all of the dimensions of an input dataset in an attempt to learn as much as possi...

Clustering is the process of partitioning a dataset into groups based on the similarity between the instances. Many clustering algorithms were proposed, but none of them proved to provide good quality partition in all situations. Consensus clustering aims to enhance the clustering process by combining different partitions obtained from different algorithms to yield a better quality consensus so...

This paper describes ongoing studies of clustering objectives and heuristics, along with their e ect on top-down partitioning based standard-cell placement. Clustering for placement has three main facets { the objective, the heuristic, and the bene ts { but the connections among these facets have never been clari ed. Our studies represent the rst steps toward reconciling these three facets. Spe...

We study the problem of clustering sequences of unlabeled point sets taken from a common metric space. Such scenarios arise naturally in applications where a system or process is observed in distinct time intervals, such as biological surveys and contagious disease surveillance. In this more general setting existing algorithms for classical (i.e. static) clustering problems are not applicable a...

Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of feature vectors (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space, whereas negative bags only contain negative instances. The classes in a MIL problem are therefore not treated in the...

Instance search is an interesting task as well a challenging issue due to the lack of effective feature representation. In this paper, instance level representation built upon fully convolutional instance-aware segmentation proposed. The ROI-pooled from segmented region. So that instances in various sizes and layouts are represented by deep features uniform length. This further enhanced use def...