Pairwise similarity coefficients are downward biased when samples only record presences and sampling is partial. A simple but forgotten index proposed by Stephen Forbes in 1907 can help solve this problem. His original equation requires knowing the number of species absent in both samples that could have been present. It is proposed that this count should simply be ignored and that the coeffici...

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the document...

Establishing accurate genetic similarity and dissimilarity between individuals is an essential and decisive point for clustering and analyzing inter and intra population diversity because different similarity and dissimilarity indices may yield contradictory outcomes. We assessed the variations caused by three commonly used similarity coefficients including Jaccard, Sorensen-Dice and Simple mat...

In this paper, different types of web session similarity metrics are compared and combined for better web session clustering. Syntactic and co-occurrence information are used for similarity calculation. Syntactic information on a web page includes the place of the page in the directory hierarchy. Co-occurrence information is the amount of the occurrences of two web pages in the same sessions. V...

root rot caused by fusarium verticillioides is one of the most important rice diseases in ilam. in order to determine genetic diversity, 56 samples were collected from rice paddies of different regions in ilam province. molecular test was carried out with a set of five pairs of ssr primers after purification and identification of isolates. the ssr primers amplified a total 26 alleles. the avera...

Clustering is one of the very powerful and widely used technique in information retrieval. All clustering methods works on finding relationship among data objects. There are various similarity measures used along with criterion functions to find similarity between documents like cosine, jaccard etc. Clustering efficiency and performance is highly dependent on the accuracy of the similarity meas...

We present a suite of algorithms for Dimension Independent Similarity Computation (DISCO) to compute all pairwise similarities between very high-dimensional sparse vectors. All of our results are provably independent of dimension, meaning that apart from the initial cost of trivially reading in the data, all subsequent operations are independent of the dimension; thus the dimension can be very ...