Relations Between Adjacency and Modularity Graph Partitioning

In this paper the exact linear relation between the leading eigenvector of the unnormalized modularity matrix and the eigenvectors of the adjacency matrix is developed. Based on this analysis a method to approximate the leading eigenvector of the modularity matrix is given, and the relative error of the approximation is derived. A complete proof of the equivalence between normalized modularity clustering and normalized adjacency clustering is also given. Some applications and experiments are given to illustrate and corroborate the points that are made in the theoretical development.Comment: 11 page

Kernelized radial basis probabilistic neural network for classification of river water quality

Radial Basis Probabilistic Neural Network (RBPNN) demonstrates broader and much more generalized capabilities which have been successfully applied to different fields.In this paper, the RBPNN is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance instead of the conventional sum-of squares distance.The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space.Through comparing the four constructed classification models with Kernelized RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as intended, results showed that, model classification on River water quality of Langat river in Selangor, Malaysia by Kernelized RBPNN exhibited excellent performance in this regard

Application of Data Mining Technique for Prediction of Academic Performance of Student A Literature survey

Application of data mining in the educational Systems can be directed to support the specific need of each of the participants in the education system and the process. Students are required to add the recommendation for additional activities, teaching material and task that would favor and improve his/her learning and the learning process. Professors would have the feedback, possibilities to classify students into group’s base on their need for guidance and monitoring, to find the mistakes, and find the effective actions. There are so many prediction model are available with difference approach and techniques in student performance prediction was reported by researcher, but there is no possibility if there are any predictors that accurately determine whether a student will be an genius, a drop out, or an average performer. The target of this study was to apply the k-map method for mining data to analyze the relationships in between students success and their behavior and to develop model for Prediction of Academic Performance of Students. This would be done by using Support Vector Machine (SVM) classifications and kernel k-map clustering mechanism. Predicting student’s performance can help identify the students who are at risk of failure and thus management can provide timely help and take essential steps to coach the students to improve performance

A Kernel-Based Membrane Clustering Algorithm

The existing membrane clustering algorithms may fail to handle the data sets with non-spherical cluster boundaries. To overcome the shortcoming, this paper introduces kernel methods into membrane clustering algorithms and proposes a kernel-based membrane clustering algorithm, KMCA. By using non-linear kernel function, samples in original data space are mapped to data points in a high-dimension feature space, and the data points are clustered by membrane clustering algorithms. Therefore, a data clustering problem is formalized as a kernel clustering problem. In KMCA algorithm, a tissue-like P system is designed to determine the optimal cluster centers for the kernel clustering problem. Due to the use of non-linear kernel function, the proposed KMCA algorithm can well deal with the data sets with non-spherical cluster boundaries. The proposed KMCA algorithm is evaluated on nine benchmark data sets and is compared with four existing clustering algorithms

Automatic Drug-Drug Interaction Detection: A Machine Learning Approach With Maximal Frequent Sequence Extraction

[EN] A Drug-Drug Interaction (DDI) occurs when the effects of a drug are modified by the presence of other drugs. DDIExtraction2011 proposes a first challenge task, Drug-Drug Interaction Extraction, to compare different techniques for DDI extraction and to set a benchmark that will enable future systems to be tested. The goal of the competition is for every pair of drugs in a sentence, decide whether an interaction is being described or not. We built a system based on machine learning based on bag of words and pattern extraction. Bag of words and other drug-level and character-level have been proven to have a high discriminative power for detecting DDI, while pattern extraction provided a moderated improvement indicating a good line for further research.This work has been done in the framework of the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. Contributions of first and second authors have been supported and partially funded by bitsnbrains S.L. Contribution of fourth author has been partially funded by the European Commission as part of the WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People Framework, by MICINN as part of the Text-Enterprise 2.0 project (TIN2009-13391-C04-03) within the Plan I+D+i. Computational resources for this research have been kindly provided by Daniel Kuehn from [email protected]ía Blasco, S.; Mola Velasco, SM.; Danger Mercaderes, RM.; Rosso, P. (2011). Automatic Drug-Drug Interaction Detection: A Machine Learning Approach With Maximal Frequent Sequence Extraction. CEUR Workshop Proceedings. 761:51-58. http://hdl.handle.net/10251/33478S515876