We are concerned with the issue of detecting changes of clustering structures from multivariate time series. From the viewpoint of the minimum description length (MDL) principle, we introduce an algorithm that tracks changes of clustering structures so that the sum of the code-length for data and that for clustering changes is minimum. Here we employ a Gaussian mixture model (GMM) as representa...

Challenges of visualization and clustering are explored with respect to sequence data from a simulation-based assessment task. Visualization issues include representing progress towards a goal and accounting for variable-length sequences. Clustering issues focus on external criteria with respect to official scoring rubrics of the same sequence data. The analysis has a confirmatory flavor; the g...

Satellite telemetry data plays an ever-important role in both the safety and reliability of a satellite. These two factors are extremely significant field space systems missions. Since it is challenging to repair orbit, health monitoring early anomaly detection approaches crucial for success A large number efficient accurate methods have been proposed aerospace but without showing enough concer...

The self-organizing map (SOM) has been widely used in many industrial applications. Classical clustering methods based on the SOM often fail to deliver satisfactory results, specially when clusters have arbitrary shapes. In this paper, through some preprocessing techniques for 4ltering out noises and outliers, we propose a new two-level SOM-based clustering algorithm using a clustering validity...

Clustering of proteins is important in the field of bioinformatics. Clustering of proteins is used for analyzing the proteins to determine their functions and structure. The number of partitioning techniques, hierarchical methods and graph-based methods are available for clustering protein sequences. In this paper, we propose a hybrid fuzzy technique for clustering proteins based on its seconda...

Word finding difficulty is a known impairments in multiple sclerosis (MS). The purpose of this study is to adapt homophone meaning generation test to Persian language, and then examine word storage and access in multiple sclerosis patients through these three word-finding tests. This study examined the word retrieval in 90 Persian speaking patients with multiple sclerosis and 90 matched healthy...

On the basis of the cluster validity function based on geometric probability in literature [1, 2], propose a cluster analysis method based on geometric probability to process large amount of data in rectangular area. The basic idea is top-down stepwise refinement, firstly categories then subcategories. On all clustering levels, use the cluster validity function based on geometric probability fi...

This study proposes a model that utilizes soft computing and Markov Chains within a data mining framework to observe the stability of customer segments. The segmentation process in this study includes clustering of existing consumers and classification-prediction of segments for existing and new customers. Both a combination and an integration of soft computing techniques were used in the propo...

The k-means method has been shown to be effective in producing good clustering results for many practical applications. However, a direct algorithm of k-means method requires time proportional to the product of number of patterns and number of clusters per iteration. This is computationally very expensive especially for large datasets. The main disadvantage of the k-means algorithm is that the ...

Hierarchical clustering algorithms are widely used in many fields of investigation. They provide a hierarchy of partitions of the same dataset. However, in many practical problems, the selection of a representative level (partition) in the hierarchy is needed. The classical approach to do so is by using a cluster validity index to select the best partition according to the criterion imposed by ...