In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...

The aim of this study is to determine the effect of word clustering method on vocabulary learning of Iranian EFL learners through a case of semantic versus phonological clustering. To this effect, 80 homogeneous students from four intermediate classes at an English institute in Torbat e Heydariyeh participated in this research. They were assigned to four groups according to semantic versus phon...

This paper proposes word clustering using word embedding. We used a neural net-based continuous skip-gram method for generating word embedding in continuous space. The proposed word clustering method represents each word in the vector space using a neural network. The K-means clustering method partitions word embedding into predetermined K-word

Abstract Online social networking services like Twitter are frequently used for discussions on numerous topics of interest, which range from mainstream and popular (e.g., music movies) to niche specialized politics). Due the popularity such services, it is a challenging task automatically model determine discussion given large amount tweets. Adding this complexity need identify these with absen...

Introduction: The Co-word analysis has the ability to identify the intellectual structure of knowledge ‎in a research domain and reveal its subsurface research aspects.‎ Objective: This study examines the intellectual structure of knowledge in the field of nanomedicine ‎during the period of 2009 to 2018 by using Co-word analysis.‎ Materials and Methods: This paper develops a sciento...

search pointers organize the main part of the application on the internet. however, because of information management hardware, high volume of data and word similarities in different fields the most answers to the user s’ questions aren`t correct. so the web graph clustering and cluster placement in corresponding answers helps user to achieve his or her intended results. community (web communit...

Word clustering is important for automatic thesaurus construction, text classification, and word sense disambiguation. Recently, several studies have reported using the web as a corpus. This paper proposes an unsupervised algorithm for word clustering based on a word similarity measure by web counts. Each pair of words is queried to a search engine, which produces a co-occurrence matrix. By cal...