Web usage mining is used to derive useful data, information, knowledge from the weblog data, and helps in identifying the user access designs for web pages.In Mining, the management of web resources, the individual is thinking about data of requests of visitors of a website that are composed as web server logs. While the content and mechanism of the set of web pages follow the intentions of the authors of the pages, the single requests shows how the users view these pages. Web usage mining can disclose relationships that were not suggested by the designer of the pages.A web server ... Read More

A hub is a set of Web pages that supports sets of links to authorities. Hub pages cannot be prominent, or there can exist some links pointing to them; however, they supports links to a set of prominent sites on a general topic.Such pages can be lists of recommended connections on single home pages, including recommended reference sites from a course home page, or professionally massed resource documents on commercial sites. Hub pages play an essential role of implicitly conferring authorities on a targeted topic.In general, a good hub is a page that points to several good authorities; a good ... Read More

Document clustering is the important techniques for organizing files in an unsupervised manner. When documents are represented as term vectors, the clustering methods can be applied. The document space is continually of large dimensionality, ranging from various hundreds to thousands.Due to the curse of dimensionality, it makes sense to first project the documents into a lowerdimensional subspace in which the semantic structure of the document space becomes clear. In the low-dimensional semantic areas, the traditional clustering algorithms can be used.There are several methods of document clustering analysis is as follows −Spectral clustering − The spectral clustering method first performs spectral ... Read More

Automated document classification is an essential text mining service because the existence of a tremendous number of on-line files, it is endless yet important to be able to automatically organize such records into classes to support document retrieval and sucessive analysis.Document classification has been used in automated topic tagging (i.e., assigning labels to documents), topic directory construction, and identification of the document writing styles and defining the goals of hyperlinks related to a set of documents.A general procedure is as follows − First, a group of preclassified files is taken as the training set. The training set is analyzed to ... Read More

Statistical spatial data analysis has been a famous techniques to exploring spatial data and analysing geographic data. The term geostatistics is related to continuous geographic area, whereas the term spatial statistics is related to discrete space.In a statistical model that manages non-spatial records, one generally consider statistical independence between different areas of data. However, different from traditional data sets, there is no such independence among spatially distributed data because in reality, spatial objects are often interrelated, or more exactly spatially colocated, in the sense that the closer the two objects are placed, the more possible they share same properties.For instance, ... Read More

A set-valued attribute can be of homogeneous or heterogeneous type. Generally, set-valued information can be generalized byGeneralization of every value in the set to its equivalent higher-level conceptDerivation of the usual behavior of the set, including the multiple elements in the set, the types or value ranges in the set, the weighted average for statistical data, or the major clusters formed by the set.Furthermore, generalization can be implemented by using several generalization operators to analyse alternative generalization paths. In this method, the result of generalization is a heterogeneous set.Example − Suppose that the hobby of a person is a set-valued ... Read More

Tuple ID propagation is an approach for implementing virtual join, which highly improves effectiveness of multirelational classification. Rather than physically joining relations, they are virtually combined by connecting the IDs of target tuples to tuples in non-target relations.In this method the predicates can be computed as if a physical join were implemented. Tuple ID propagation is flexible and effectiveness, because IDs can simply be propagated between some two relations, needing only small amounts of data transfer and more storage space. By doing so, predicates in multiple relations can be computed with small redundant computation.Tuple ID propagation must be enforced with ... Read More

The BLAST algorithm was produced by Altschul, Gish, Miller, around 1990 at the National Center for Biotechnology Information (NCBI). BLAST is used to derive functional and evolutionary relationships among sequences and to help recognize members of gene families.The NCBI website includes several common BLAST databases. As per their content, they are combined into nucleotide and protein databases. NCBI also supports specialized BLAST databases including the vector screening database, there are several genome databases for multiple organisms, and trace databases.BLAST uses a heuristic approaches to discover the largest local alignments between a query sequence and a database. BLAST increase the complete ... Read More

The alignment depends on the fact that all living organisms are associated by evolution. This uses that the nucleotide (DNA, RNA) and proteins series of the species that are nearer to each other in evolution must exhibit higher similarities.An alignment is the phase of lining up sequences to obtain a maximal level of identity, which also defines the degree of similarity among sequences. There are two sequences are homologous if they send a common ancestor.The degree of similarity acquired by sequence alignment can be beneficial in deciding the possibility of homology among two sequences. Such an alignment support decide the ... Read More

GSP stands for Generalised Sequential Patterns. It is a sequential pattern mining method that was produced by Srikant and Agrawal in 1996. It is an expansion of their seminal algorithm for usual itemset mining, referred to as Apriori. GSP needs the downward-closure natures of sequential patterns and adopts a several-pass, students create-and-test approach.The algorithm is as follows. In the first scan of the database, it can discover some frequent items, i.e., those with minimum support. Each item yields a 1-event frequent sequence including that item. Each subsequent pass begins with a seed group of sequential patterns and the group of ... Read More