Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam

High Quality, Efficient Hierarchical Document Clustering Using Closed Interesting Itemsets

High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to improve the efficiency of hierarchical document clustering. In this paper, we introduce the notion of "closed interesting" itemsets (i.e. closed itemsets with high interestingness). We provide heuristics such as "super item" to efficiently mine these itemsets and show that they provide significant dimensionality reduction over closed frequent itemsets. Using "closed interesting" itemsets, we propose a new hierarchical document clustering method that outperforms state of the art agglomerative, partitioning and frequent-itemset based methods both in terms of FScore and Entropy, without requiring dataset specific parameter tuning. We evaluate twenty interestingness measures on nine standard datasets and show that when used to generate "closed interesting" itemsets, and to select parent nodes, Mutual Information, Added Value, Yule's Q and Chi-Square offers best clustering performance, regardless of the characteristics of underlying dataset. We also show that our method is more scalable, and results in better run-time performance as compare to leading approaches. On a dual processor machine, our method scaled sub-linearly and was able to cluster 200K documents in about 40 seconds

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed