presents a method of ontology-based sentiment clustering to cluster and analyse the movie reviews. In this paper, we proposed the domain ontology to extract the related generic category, in order to find the class of the movie based on the

The purpose of this study was to propose and test a motivational model of high school dropout. The model posits that teachers, parents, and the school administration's behaviors toward students influence students' perceptions of competence and autonomy. The less autonomy supportive

causality analysis, we show that the rFIC-ACC network, and the rFIC, in particular, plays a critical and causal role in switching between the CEN and the DMN. We replicated this causal connectivity pattern in two additional experiments: (i) a visual attention ''oddball'' task and (ii) a

-English language texts, e.g., Chinese research proposals. This paper presents a novel ontology-based text-mining approach to cluster research proposals based on their similarities in research areas. The method is efficient and effective for clustering research proposals with both English and Chinese texts

time and have strong requirements on running environment. Based on the MapReduce framework and the virtual doc-ument technique, in this paper, we propose a 3-stage MapReduce-based approach called V-Doc+ for matching large ontologies, which signifi-cantly reduces the run time while keeping good

. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization. We review semantic similarity measures applied to biomedical

for cluster analysis. However, the KM and FCM algorithms have considerable trouble in a noisy environment and are inaccurate with large numbers of different sample sized clusters. The Kernel based Fuzzy C-Means (KFCM) clustering is moreover studied with associated cluster validity measures. Many numerical

The R package clValid contains functions for validating the results of a clustering analysis. There are three main types of cluster validation measures available, “internal”, “stability”, and “biological”. The user can choose from nine clustering algorithms in existing R packages, including

-vs.all comparison of SWISSPROT, gives a very conservative initial classification based on the highest scoring pairs. The many classes in this classification correspond to protein subfamilies. Subsequently we merge the subclasses using the weaker pairs in a two-phase clustering algorithm. The algorithm makes use

as visual analysis of mappings between ontology- and cluster-based space-filling representations. In this context, we discuss our approach together with specific properties of the biological input data and identify features that make our approach easily usable for domain experts.