Terms Of Centroid Research Articles

Arabic Tweets Clustering and Labeling Based on Lingual and Semantically Enriched Bayesian Network Model

Knowing the hot topics in Social Networking Systems become essential for many applications such as marketing and social studies. Topic detection from Arabic posts or tweets using standard cluster labeling techniques such as the most frequent terms or the most predictive terms in the cluster lack the accuracy to catch the implicit semantic relations between terms. In this paper, in this approach we automatically cluster the Arabic tweets based on lingual clustering model for Arabic. This model is based on the Arabic’s rich morphology where most of the words are root derivatives. Changing vowels and insertion of consonants lead to the generation of inflections and derivations. In addition, our approach introduces a semantically enriched Bayesian Network model for cluster labeling. This model generates a list of candidate labels for each cluster given its centroid terms. This model enriches the centroid terms semantic from world knowledge, and models the semantic relations between them by creating a Bayesian Network. After that it utilized probabilistic inference techniques to elect a list of labels to the candidate topic. The elected lists have the highest posterior probability as observed evidences taking into consideration the explicit and implicit features of the cluster’s centroid terms. We conducted a primitive evaluation using manually annotated data set. Our data set consists of 100 clusters that extracted from 38,000 tweets. The results showed that our approach has indications for effectively labeling clusters of tweets in Social Networking Systems. Keywords: Arabic, bayesian network, semantic, stemmer.

A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition.

BackgroundHuman Papillomavirus (HPV) genotyping is an important approach to fight cervical cancer due to the relevant information regarding risk stratification for diagnosis and the better understanding of the relationship of HPV with carcinogenesis. This paper proposed two new feature extraction techniques, i.e. ChaosCentroid and ChaosFrequency, for predicting HPV genotypes associated with the cancer. The additional diversified 12 HPV genotypes, i.e. types 6, 11, 16, 18, 31, 33, 35, 45, 52, 53, 58, and 66, were studied in this paper.In our proposed techniques, a partitioned Chaos Game Representation (CGR) is deployed to represent HPV genomes. ChaosCentroid captures the structure of sequences in terms of centroid of each sub-region with Euclidean distances among the centroids and the center of CGR as the relations of all sub-regions. ChaosFrequency extracts the statistical distribution of mono-, di-, or higher order nucleotides along HPV genomes and forms a matrix of frequency of dots in each sub-region. For performance evaluation, four different types of classifiers, i.e. Multi-layer Perceptron, Radial Basis Function, K-Nearest Neighbor, and Fuzzy K-Nearest Neighbor Techniques were deployed, and our best results from each classifier were compared with the NCBI genotyping tool.ResultsThe experimental results obtained by four different classifiers are in the same trend. ChaosCentroid gave considerably higher performance than ChaosFrequency when the input length is one but it was moderately lower than ChaosFrequency when the input length is two. Both proposed techniques yielded almost or exactly the best performance when the input length is more than three. But there is no significance between our proposed techniques and the comparative alignment method.ConclusionsOur proposed alignment-free and scale-independent method can successfully transform HPV genomes with 7,000 - 10,000 base pairs into features of 1 - 11 dimensions. This signifies that our ChaosCentroid and ChaosFrequency can be served as the effective feature extraction techniques for predicting the HPV genotypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0493-4) contains supplementary material, which is available to authorized users.

Evidence for subduction beneath Gibraltar Arc and Andean regions from k-means earthquake centroids

k-Means algorithms are widely used for determining clusters in broad types of datasets. Since zones of high seismic activity as plate boundary present diffuse seismicity patterns, the use of k-algorithm is a way to classify earthquakes in terms of centroids. Mapping centroids improves seismic visibility for further tectonic interpretation. We used selected datasets of earthquakes and determined the number of clusters or values of k by introducing the silhouette index method to check the validity of cluster numbers. By introducing magnitude size in the vectorial attributes, k-means algorithm provides a map of centroids that represents the location of high seismic energy, which is useful in seismic risk assessment. By including the depth of seismic events as the main attribute, we obtained spatiotemporal variations of centroids, which improve the image resolution of seismicity at depth to find out the underlying dynamic process. This has been achieved in subduction zone of Chile where the presence of slab is reflected by centroid distribution. The method is particularly relevant to complex seismic zones where controversial geodynamic models are reported such as the Gibraltar Arc. Resulting model supports W-oriented subduction underlying many parts of the Gibraltar zone.

Sibilant production patterns in three generations of Guoyu-Taiwanese bilinguals

This study investigates the effects of age and language dominance on sibilant production in a bilingual community. Guoyu (Taiwanese Mandarin) has 3 sibilants: alveolar /s/, retroflex /ʂ/ and alveolo-palatal /ɕ/, while Taiwanese (a Southern Min dialect) only has /s/, which is palatalized before /i/ and /io/. Productions of sibilant initial words were elicited using a word repetition task. Subjects were 30 adults in three age bands from 20-80 years, with the oldest being the most Taiwanese-dominant. The spectral centroid was obtained from the middle 40ms of each sibilant, along with the onset F2 of the following vowel. In the low-vowel /a/ context, the youngest speakers clearly separate /s/ in both languages from the Guoyu /ʂ/ and /ɕ/ along the centroid dimension. The /ʂ/ is then separated from /ɕ/ by F2. However, the oldest speakers show no clear separation of these sounds in terms of centroid, although Guoyu /ɕ/ can still be differentiated by F2. Also, the three generations demonstrated differences in the assimilation patterns of palatal sounds. The younger Guoyu-dominant speakers assimilated Taiwanese palatalized one to Guoyu /ɕ/ in both centroid and F2, while older speakers matched the Guoyu /s/~/ɕ/ distinction to that of the Taiwanese pattern.