The assessment of visible differences in leaf shape between plant species or mutants (phenotyping) plays a significant role in plant research. This paper investigates the application of unsupervised data clustering techniques for phenotype screening to find hidden common shape categories. A set of two wildtypes and seven mutations of Arabidopsis acted as a test case. K-Means, NG, GNG, SOM and A...

In this paper, we define and study a new notion of stability for the k-means clustering scheme building upon the field of quantization of a probability measure. We connect this definition of stability to a geometric feature of the underlying distribution of the data, named absolute margin condition, inspired by recent works on the subject.

This paper presents the integration between the quantitative target approximation (qTA) model and the unsupervised clustering technique to study Thai tones. The qTA model simulates F0 production on the basis of articulation process. Parameters extracted from the F0 of Thai speech by analysisand-synthesis method were further analyzed by K-means clustering. The number and form of pitch target wer...

In this paper, we explored how to use meta-data information in information retrieval task. We presented a new language model that is able to take advantage of the category information for documents to improve the retrieval accuracy. We compared the new language model with the traditional language model over the TREC4 dataset where the collection information for documents is obtained using the k...

At present, there are many online learning systems which are available and provided on the Internet. Almost all of these systems are static contents and test banks. To improve the teaching and learning on the Internet, this study proposes a new learning system that can be adjusted to the knowledge and understanding of students. This system uses K-Means Algorithm to cluster the student by pretes...

In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at...

An optimal data partitioning in parallel/distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd-Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimen...

Support Vector Data Description (SVDD) has a limitation for dealing with a large data set in which computational load drastically increases as training data size becomes large. To handle this problem, we propose a new fast SVDDmethod using K-means clustering method. Our method uses divide-and-conquer strategy; trains each decomposed subproblems to get support vectors and retrains with the suppo...

In this paper two techniques for units clustering and factorial dimensionality reduction of variables and occasions of a three-mode data set are discussed. These techniques can be seen as the simultaneous version of two procedures based on the sequential application of k-means and Tucker2 algorithms and vice versa. The two techniques, T3Clus and 3Fk-means, have been compared theoretically and e...