In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...

Brown clustering is a hard, hierarchical, bottom-up clustering of words in a vocabulary. Words are assigned to clusters based on their usage pattern in a given corpus. The resulting clusters and hierarchical structure can be used in constructing class-based language models and for generating features to be used in natural language processing (NLP) tasks. Because of its high computational cost, ...

The current state-of-the-art methods in 3D instance segmentation typically involve a clustering step, despite the tendency towards heuristics, greedy algorithms, and lack of robustness to changes data statistics. In contrast, we propose fully-convolutional point cloud method that works per-point prediction fashion. doing so it avoids challenges clustering-based face: introducing dependencies am...

We investigate clustering techniques that are speci cally tailored for object-oriented database systems. Unlike traditional database systems object-oriented data models incorporate the application behavior in the form of type-associated operations. This source of information is exploited for clustering decisions by statically determining the operations' access behavior applying data ow analysis...

Non-maximum suppression (NMS) is used in virtually all state-of-the-art object detection pipelines. While essential object detection ingredients such as features, classifiers and proposal methods have been extensively researched it it surprising how little work has aimed to systematically address NMS. The de-facto standard for NMS is based on greedy clustering with a fixed distance threshold, w...

In Web Search Engine, Clustering is an efficient way of reaching information from raw data and K-means is a basic method for it. Although it is easy to implement and understand, but it has serious drawbacks. So we go for some other techniques for filtering process like greedy global algorithm. These types of algorithms are also work as a text mining techniques over the web and also cluster the ...

Bipartite network is a branch of complex network. It is widely used in many applications such as social network analysis, collaborative filtering and information retrieval. Partitioning a bipartite network into smaller modules helps to get insight of the structure of the bipartite network. The main contributions of this paper include: (1) proposing an MDL 21 criterion for identifying a good par...

Clustering is an important task in the process of data analysis which can be viewed as a data modeling technique that provides an attractive mechanism to automatically find the hidden structure of large data sets (Jain et al., 1999). Informally, this task consists of the division of data items (objects, instances, etc.) into groups or categories, such that all objects in the same group are simi...

Bipartite network is a branch of complex network. It is widely used in many applications such as social network analysis, collaborative filtering and information retrieval. Partitioning a bipartite network into smaller modules helps to get insight of the structure of the bipartite network. The main contributions of this paper include: (1) proposing an MDL 21 criterion for identifying a good par...

In this paper, we attempt to tackle the MediaEval 2013 Retrieving Diverse Social Images challenge, which is a filter and refinement problem on a Flickr-based ranked set of social images. We developed three different approaches, using visual data, textual data and a combination thereof, respectively. Hierarchical clustering on highly relevant images, combined with a greedy approach to complement...