Log analysis shows that PubMed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity may lead to irrelevant retrieval results. To improve the PubMed user experience with author name queries, we designed an author name disambiguation system consisting of similarity estimation and agglomerative clustering. A machine-learning method was e...

In this paper we apply different techniques of information distortion on a set of classical books written in English. We study the impact that these distortions have upon the Kolmogorov complexity and the clustering by compression technique (the latter based on Normalized Compression Distance, NCD). We show how to decrease the complexity of the considered books introducing several modifications...

Abstract ─ A wide range of clustering algorithms is available in literature and still an open area for researcher’s k-means algorithm is one of the basic and most simple partitioning clustering technique is given by Macqueen in 1967. A new clustering algorithm used in this paper is affinity propagation. The number of cluster k has been supplied by the user and the Affinity propagation found clu...

Image de-noising and clustering in medical images are quite complex because of narrow dynamic range and in-homogeneity. Pre processing steps like image de-noising do have influence over the subsequent image processing which misleads further image analysis. In this paper, a new method which incorporates the advantages of adaptive center weighted median filter and hybrid median filter, called Ite...

Deep sequencing of PCR amplicon libraries facilitates the detection of low-abundance populations in environmental DNA surveys of complex microbial communities. At the same time, deep sequencing can lead to overestimates of microbial diversity through the generation of low-frequency, error-prone reads. Even with sequencing error rates below 0.005 per nucleotide position, the common method of gen...

Intelligent Autonomous systems (IAS) continuously receive large streams of diverse data from numerous entities operating and interacting in their environment. It is vital that the learning models in IAS to scale up to the new and unknown data items that were not present in the training or testing datasets. Scalable learning is nothing but a method to achieve maximum classification without rejec...

In this paper we apply different techniques of information distortion on a set of classical books written in English. We study the impact that these distortions have upon the Kolmogorov complexity and the clustering by compression technique (the latter based on Normalized Compression Distance, NCD). We show how to decrease the complexity of the considered books introducing several modifications...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

Agglomerative hierarchical clustering (AHC) is an unsupervised classification strategy of merging the closest pair of clusters recursively, and has been widely used in speaker diarization systems to classify speech segments by speaker identity. The most critical part in AHC is how to automatically stop the recursive process at the point when clustering error rate reaches its lowest possible val...

*Correspondence: [email protected] Institute of Engineering and Computational Mechanics, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany Erratum to: Adv. Model. and Simul. in Eng. Sci. (2016) 3:20 DOI 10.1186/s40323-016-0072-x In the publication of this article [1], there was an error in the “Simple alternatives for clustering” section which was published w...