The problem of unsupervised and semi-supervised clustering is extensively studied in machine learning. In order to involve user in image data clustering, (Lai et al., 2014) proposed a new approache for interactive semi-supervised clustering that translates the feedback of user (expressed at the level of individual images) into pairwise constraints between groups of images, these groupes being c...

Hyperspectral image (HSI) clustering has drawn increasing attention due to its challenging work with respect to the curse of dimensionality. In this paper, we propose a novel class probability propagation of supervised information based on sparse subspace clustering (CPPSSC) algorithm for HSI clustering. Firstly, we estimate the class probability of unlabeled samples by way of partial known sup...

High dimensional data clustering arises naturally in a lot of domains, and have regularly presented a great deal with for usual data mining techniques. In this paper, presents an optimal perspective on the problem of Consensus Clustering in high-dimensional data. The proposed method called ―Fuzzy based and kernel mappings with Consensus Neighboring clustering (FKCNC)‖, which takes as key measur...

With the advent of the Internet and low-price digital cameras, as well as powerful image editing software, the authenticity of digital images can no longer be taken for granted. Image noises are often introduced into the tampered region during image manipulation process. In this paper, we propose a detection method to locate image forgeries based on noise estimation on HSV color space and hybri...

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectro...

Self-Organized Maps (SOMs) are a popular approach for clustering data. However, most SOM based approaches ignore prior knowledge about potential categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the K...

The large increase in multimedia data volume requires the development of effective solutions for visual exploration of multimedia databases. After reviewing the visualization process involved, we emphasis the need of data structuration. The main objective of this thesis is to propose and study clustering and classification of multimedia database for their visual exploration. We begin with a sta...