Nonadaptive processes in primate and human evolution
... In this article, I explore the genetic and genomic evidence that indicates a relative augmentation in the power of random genetic drift in relation to natural selection in primate and human evolution. There are two central questions I explore. First, what is the evidence that genetic drift has playe ...
... In this article, I explore the genetic and genomic evidence that indicates a relative augmentation in the power of random genetic drift in relation to natural selection in primate and human evolution. There are two central questions I explore. First, what is the evidence that genetic drift has playe ...
Design and Development of Novel Sentence Clustering Technique
... words and documents to entire languages. They are popular tools to use to group similar items together. Irrespective of the specific task (e.g., summarization, text mining, etc.), most documents will contain interrelated topics or themes, and many sentences will be related to some degree to a number ...
... words and documents to entire languages. They are popular tools to use to group similar items together. Irrespective of the specific task (e.g., summarization, text mining, etc.), most documents will contain interrelated topics or themes, and many sentences will be related to some degree to a number ...
View/Open - ScholarWorks
... With the increasing availability of user data, efforts to identify user interests by sentiment analysis of review data, and the application of these results to make recommendations have received more attention. Over time, as the volume of data has grown by several degrees of magnitude, and as techni ...
... With the increasing availability of user data, efforts to identify user interests by sentiment analysis of review data, and the application of these results to make recommendations have received more attention. Over time, as the volume of data has grown by several degrees of magnitude, and as techni ...
HG3212991305
... the original k-means has sum-of-squared-error objective function that uses Euclidean distance. In a very sparse and high-dimensional domain like text documents, spherical k-means, which uses cosine similarity (CS) instead of Euclidean distance as the measure, is deemed to be more suitable .In, Baner ...
... the original k-means has sum-of-squared-error objective function that uses Euclidean distance. In a very sparse and high-dimensional domain like text documents, spherical k-means, which uses cosine similarity (CS) instead of Euclidean distance as the measure, is deemed to be more suitable .In, Baner ...
Locally adaptive metrics for clustering high dimensional data
... important issue in data compression, signal coding, pattern classification, and function approximation tasks. Clustering suffers from the curse of dimensionality problem in high-dimensional spaces. In high dimensional spaces, it is highly likely that, for any given pair of points within the same clu ...
... important issue in data compression, signal coding, pattern classification, and function approximation tasks. Clustering suffers from the curse of dimensionality problem in high-dimensional spaces. In high dimensional spaces, it is highly likely that, for any given pair of points within the same clu ...
Clustering Data with Measurement Errors
... expenditure, the data for geographical regions could be estimates of average household income and average expenditure. A sample average by itself is inadequate and can be misleading unless the sampling error for each region is negligible. Sampling error, which can be estimated as the standard deviat ...
... expenditure, the data for geographical regions could be estimates of average household income and average expenditure. A sample average by itself is inadequate and can be misleading unless the sampling error for each region is negligible. Sampling error, which can be estimated as the standard deviat ...
Study on Feature Selection Methods for Text Mining
... proposed a text categorization method on using partial supervision. Clustering is used to create categories and it can be used for document classification. The documents are clustered based on supervised clustering algorithm then the clustered documents are categorized based on categorization algori ...
... proposed a text categorization method on using partial supervision. Clustering is used to create categories and it can be used for document classification. The documents are clustered based on supervised clustering algorithm then the clustered documents are categorized based on categorization algori ...
Clustering System based on Text Mining using the K
... Lemmatisation (or lemmatization) in linguistics, is the process of reducing the inflected forms or sometimes the derived forms of a word to its base form so that they can be analysed as a single term. In computational linguistic, lemmatisation is the algorithmic process of getting the normalized or ...
... Lemmatisation (or lemmatization) in linguistics, is the process of reducing the inflected forms or sometimes the derived forms of a word to its base form so that they can be analysed as a single term. In computational linguistic, lemmatisation is the algorithmic process of getting the normalized or ...
Semi-supervised clustering methods
... K-means clustering is an example of what are known as partitional clustering methods, which partition a data set into a fixed number of disjoint subgroups. In contrast, hierarchical clustering groups data points into a series of clusters in a tree-like structure. At each level of the tree, clusters ...
... K-means clustering is an example of what are known as partitional clustering methods, which partition a data set into a fixed number of disjoint subgroups. In contrast, hierarchical clustering groups data points into a series of clusters in a tree-like structure. At each level of the tree, clusters ...
Hierarchical Clustering
... Center-based – A cluster is a set of objects such that an object in a cluster is closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representat ...
... Center-based – A cluster is a set of objects such that an object in a cluster is closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representat ...
Subspace Clustering for High Dimensional Data: A Review
... clusters have µ = 0 and σ = 1. The second two clusters are in dimensions b and c and were generated in the same manner. The data can be seen in Figure 2. When k-means is used to cluster this sample data, it does a poor job of finding the clusters. This is because each cluster is spread out over some ...
... clusters have µ = 0 and σ = 1. The second two clusters are in dimensions b and c and were generated in the same manner. The data can be seen in Figure 2. When k-means is used to cluster this sample data, it does a poor job of finding the clusters. This is because each cluster is spread out over some ...
Human genetic clustering
Human genetic clustering analysis uses mathematical cluster analysis of the degree of similarity of genetic data between individuals and groups in order to infer population structures and assign individuals to groups. These groupings in turn often, but not always, correspond with the individuals' self-identified geographical ancestry. A similar analysis can be done using principal components analysis, which in earlier research was a popular method. Many studies in the past few years have continued using principal components analysis.