The Possibilistic Latent Variable (PLV) clustering algorithm is a powerful tool for the analysis of complex datasets due to its robustness toward data distributions of different types and its ability to accurately identify the inherent clusters within the data. The scaling coefficient in the PLV algorithm plays a key role in reducing the effects of noise, thereby improving the precision of the clustering results. However, the optimal value of the scaling parameter varies depending on the population type of dataset. Accordingly, the current study proposes an evaluation method for evaluating suitable values of the scaling parameter. The relative comparison of each method is then examined by conducting PLV clustering trials using datasets comprising data of different types and patterns.

When analyzing the results of microarray experiments, biologists generally use unsupervised categorization tools. However, such tools regard each time point as an independent dimension and utilize the Euclidean distance to compute the similarities between expressions. Furthermore, some of these methods require the number of clusters to be determined in advance, which is clearly impossible in the case of a new dataset. Therefore, this study proposes a novel scheme, designated as the Variation-based Coexpression Detection (VCD) algorithm, to analyze the trends of expressions based on their variation over time. The proposed algorithm has two advantages. First, it is unnecessary to determine the number of clusters in advance since the algorithm automatically detects those genes whose profiles are grouped together and creates patterns for these groups. Second, the algorithm features a new measurement criterion for calculating the degree of change of the expressions between adjacent time points and evaluating their trend similarities. Three real-world microarray datasets are employed to evaluate the performance of the proposed algorithm.

This paper proposes a new clustering algorithm referred to as the possibilitic latent variables (PLV) clustering algorithm. This algorithm provides a powerful tool for the analysis of complex data, such as clinical diagnosis and biological expressions data, due to its robustness to various data distributions and its accuracy in establishing appropriate groups from data. The algorithm combines a distribution model and the fuzzy degrees concept. Compared to the expectation-maximization (EM) algorithm, which is a well-known distribution estimating algorithm, the PLV algorithm has the considerable advantage that it can be applied to various data types, i.e. it is not restricted solely to Gaussian data distributions. Additionally, the proposed algorithm has a better performance than the well-known fuzzy clustering algorithm, i.e. the FCM algorithm, where it can address compact regions, other than simply dividing objects into several equal populations. The performance of the proposed algorithm is verified by conducting clustering tasks on the contents of several medical diagnosis and biological expressions datasets.

We have developed a gene-gene (G-G) relation browser that combines fuzzy sequential pattern mining and information-extraction model to extract from biomedical literature knowledge on gene-gene interactions. Our approach aims to detect associated G-G relations that are often discussed in documents. Integration of the related relations lead to an individual G-G network. Graphic presentations are to be used to demonstrate the relationships between gene products.

This paper presents a new fuzzy cover-based clustering algorithm. In the proposed algorithm, the concept of fuzzy cover and objective function are employed to identify holding points in the dataset, and we associate these holding points together to build up the backbones of the final clusters. Three specific objectives underlie the presentation of the proposed approach in this paper. The first is to describe mathematical formulation of the fuzzy covers, and the second is to summarize the detailed procedure of constructing fuzzy covers and splicing them into clusters. The third goal is to demonstrate that this approach is able to find out reasonable representative patterns in the final clusters. We illustrate this approach with four examples in order to verify the clustering effectiveness.