Data Mining Articles - Page 6 of 36

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

Sequential pattern mining is the mining of frequently appearing series events or subsequences as patterns. An instance of a sequential pattern is users who purchase a Canon digital camera are to purchase an HP color printer within a month.For retail information, sequential patterns are beneficial for shelf placement and promotions. This industry, and telecommunications and different businesses, can also use sequential patterns for targeted marketing, user retention, and several tasks.There are several areas in which sequential patterns can be used such as Web access pattern analysis, weather prediction, production processes, and web intrusion detection.Given a set of sequences, where each ... Read More

STREAM is an individual-pass, constant element approximation algorithm that was produced for the k-medians problem. The k-medians problem is to cluster N data points into k clusters or groups such that the sum squared error (SSQ) between the points and the cluster center to which they are assigned is minimized. The idea is to assign similar points to the same cluster, where these points are dissimilar from points in other clusters.In the stream data model, data points can only be seen once, and memory and time are limited. It can implement high-quality clustering, the STREAM algorithm processes data streams in ... Read More

Data stream clustering is described as the clustering of data that appar continuously including telephone data, multimedia data, monetary transactions etc. Data stream clustering is generally treated as a streaming algorithm and the objective is, given a sequence of points, to make a best clustering of the stream, utilizing a small amount of memory and time.Some applications needed the automated clustering of such data into set based on their similarities. Examples contains applications for web intrusion detection, analyzing Web clickstreams, and stock market analysis.There are several dynamic methods for clustering static data sets clustering data streams places additional force on ... Read More

A user supports two input parameters including the min support threshold, σ, and the error bound previously, indicated as ε. The incoming stream is theoretically divided into buckets of width w = [1/ε].Let N be the current stream length, i.e., the number of items view so far. The algorithm needs a frequency-list data structure for all elements with frequency higher than 0. For every item, the list supports f, the approximate frequency count, and ∆, the maximum possible error of f.The algorithm procedure buckets of items as follows. When a new bucket arrives in, the items in the bucket are ... Read More

Randomized Algorithms − Randomized algorithms in the form of random sampling and blueprint, are used to deal with large, high-dimensional data streams. The need of randomization leads to simpler and more effective algorithms in contrast to known deterministic algorithms.If a randomized algorithm continually returns the correct answer but the running times change, it is called a Las Vegas algorithm. In contrast, a Monte Carlo algorithm has bounds on the running time but cannot restore the true result. It can usually consider Monte Carlo algorithms. The importance of a randomized algorithm is simply as a probability distribution over a group of ... Read More

The sequential exception technique simulates the method in which humans can distinguish unusual sets from between a sequence of supposedly like objects. It helps implicit redundancy of the data.Given a data set, D, of n objects, it construct a sequence of subsets, {D1, D2, ..., Dm}, of these objects with 2 ≤ m ≤ n including$$\mathrm{D_{j−1}\subset D_{j}\:\:where\: D_{j}\subseteq D}$$Dissimilarities are assessed between subsets in the series. The technique learns the following terms which are as follows −Exception set − This is the set of deviations or outliers. It is defined as the smallest subset of objects whose removal results in ... Read More