A Fuzzy Semisupervised Clustering Method: Application to the Classification of Scientific Publications
نویسندگان
چکیده
This paper introduces a new method of fuzzy semisupervised hierarchical clustering using fuzzy instance level constraints. It introduces the concepts of fuzzy must-link and fuzzy cannot-link constraints and use them to find the optimum α-cut of a dendrogram. This method is used to approach the problem of classifying scientific publications in web digital libraries. It is tested on real data from that problem against classical methods and crisp semisupervised hierarchical clustering.
منابع مشابه
Modification of the Fast Global K-means Using a Fuzzy Relation with Application in Microarray Data Analysis
Recognizing genes with distinctive expression levels can help in prevention, diagnosis and treatment of the diseases at the genomic level. In this paper, fast Global k-means (fast GKM) is developed for clustering the gene expression datasets. Fast GKM is a significant improvement of the k-means clustering method. It is an incremental clustering method which starts with one cluster. Iteratively ...
متن کاملProposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...
متن کاملOil Reservoirs Classification Using Fuzzy Clustering (RESEARCH NOTE)
Enhanced Oil Recovery (EOR) is a well-known method to increase oil production from oil reservoirs. Applying EOR to a new reservoir is a costly and time consuming process. Incorporating available knowledge of oil reservoirs in the EOR process eliminates these costs and saves operational time and work. This work presents a universal method to apply EOR to reservoirs based on the available data by...
متن کاملA Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...
متن کاملEnhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification
A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the c...
متن کامل