Sequential Pattern Mining
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Sequential Pattern Mining
Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity. Sequential pattern mining is a special case of structured data mining. There are several key traditional computational problems addressed within this field. These include building efficient databases and indexes for sequence information, extracting the frequently occurring patterns, comparing sequences for similarity, and recovering missing sequence members. In general, sequence mining problems can be classified as ''string mining'' which is typically based on string processing algorithms and ''itemset mining'' which is typically based on association rule learning. ''Local process models'' extend sequential pattern mining to more complex patterns that can inclu ...
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Insertion (genetics)
In genetics, an insertion (also called an insertion mutation) is the addition of one or more nucleotide base pairs into a DNA sequence. This can often happen in microsatellite regions due to the DNA polymerase slipping. Insertions can be anywhere in size from one base pair incorrectly inserted into a DNA sequence to a section of one chromosome inserted into another. The mechanism of the smallest single base insertion mutations is believed to be through base-pair separation between the template and primer strands followed by non-neighbor base stacking, which can occur locally within the DNA polymerase active site. On a chromosome level, an ''insertion'' refers to the insertion of a larger sequence into a chromosome. This can happen due to unequal crossover during meiosis. N region addition is the addition of non-coded nucleotides during recombination by terminal deoxynucleotidyl transferase. P nucleotide insertion is the insertion of palindromic sequences encoded by the ends ...
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GitHub
GitHub, Inc. () is an Internet hosting service for software development and version control using Git. It provides the distributed version control of Git plus access control, bug tracking, software feature requests, task management, continuous integration, and wikis for every project. Headquartered in California, it has been a subsidiary of Microsoft since 2018. It is commonly used to host open source software development projects. As of June 2022, GitHub reported having over 83 million developers and more than 200 million repositories, including at least 28 million public repositories. It is the largest source code host . History GitHub.com Development of the GitHub.com platform began on October 19, 2007. The site was launched in April 2008 by Tom Preston-Werner, Chris Wanstrath, P. J. Hyett and Scott Chacon after it had been made available for a few months prior as a beta release. GitHub has an annual keynote called GitHub Universe. Organizational ...
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GSP Algorithm
GSP algorithm (''Generalized Sequential Pattern'' algorithm) is an algorithm used for sequence mining. The algorithms for solving sequence mining problems are mostly based on the '' apriori'' (level-wise) algorithm. One way to use the level-wise paradigm is to first discover all the frequent items in a level-wise fashion. It simply means counting the occurrences of all singleton elements in the database. Then, the transactions are filtered by removing the non-frequent items. At the end of this step, each transaction consists of only the frequent elements it originally contained. This modified database becomes an input to the GSP algorithm. This process requires one pass over the whole database. GSP algorithm makes multiple database passes. In the first pass, all single items (1-sequences) are counted. From the frequent items, a set of candidate 2-sequences are formed, and another pass is made to identify their frequency. The frequent 2-sequences are used to generate the candidate ...
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Buying Pattern
Consumer behavior is the study of individuals, groups, or organizations and all the activities associated with the purchase, use and disposal of goods and services. Consumer behaviour consists of how the consumer's emotions, attitudes, and preferences affect buying behaviour. Consumer behaviour emerged in the 1940–1950s as a distinct sub-discipline of marketing, but has become an interdisciplinary social science that blends elements from psychology, sociology, social anthropology, anthropology, ethnography, ethnology, marketing, and economics (especially behavioural economics). The study of consumer behaviour formally investigates individual qualities such as demographics, personality lifestyles, and behavioural variables (such as usage rates, usage occasion, loyalty, brand advocacy, and willingness to provide referrals), in an attempt to understand people's wants and consumption patterns. Consumer behaviour also investigates on the influences on the consumer, from soc ...
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Apriori Algorithm
AprioriRakesh Agrawal and Ramakrishnan SrikanFast algorithms for mining association rules Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487-499, Santiago, Chile, September 1994. is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. Overview The Apriori algorithm was proposed by Agrawal and Srikant in 1994. Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation or IP addresses). Other algorithms are de ...
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Association Rule Learning
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.Piatetsky-Shapiro, Gregory (1991), ''Discovery, analysis, and presentation of strong rules'', in Piatetsky-Shapiro, Gregory; and Frawley, William J.; eds., ''Knowledge Discovery in Databases'', AAAI/MIT Press, Cambridge, MA. In any given transaction with a variety of items, association rules are meant to discover the rules that determine how or why certain items are connected. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule \ \Rightarrow \ found in the sales data of a supermarket would indicate that if a customer buys ...
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Sequence Alignment
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as calculating the distance cost between strings in a natural language or in financial data. Interpretation If two sequences in an alignment share a common ancestor, mismatches can be interpreted as point mutations and gaps as indels (that is, insertion or deletion mutations) introduced in one or both lineages in the time since they diverged from one another. In sequence alignments of proteins, the degree of similarity between amino acids occupying a parti ...
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ClustalW
Clustal is a series of widely used computer programs used in bioinformatics for multiple sequence alignment. There have been many versions of Clustal over the development of the algorithm that are listed below. The analysis of each tool and its algorithm are also detailed in their respective categories. Available operating systems listed in the sidebar are a combination of the software availability and may not be supported for every current version of the Clustal tools. Clustal Omega has the widest variety of operating systems out of all the Clustal tools. History There have been many variations of the Clustal software, all of which are listed below: * Clustal: The original software for multiple sequence alignments, created by Des Higgins in 1988, was based on deriving phylogenetic trees from pairwise sequences of amino acids or nucleotides. * ClustalV: The second generation of the Clustal software was released in 1992 and was a rewrite of the original Clustal package. It int ...
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BLAST
Blast or The Blast may refer to: * Explosion, a rapid increase in volume and release of energy in an extreme manner *Detonation, an exothermic front accelerating through a medium that eventually drives a shock front Film * ''Blast'' (1997 film), starring Andrew Divoff * ''Blast'' (2000 film), starring Liesel Matthews * ''Blast'' (2004 film), an action comedy film * ''Blast!'' (1972 film) or ''The Final Comedown'', an American drama * ''BLAST!'' (2008 film), a documentary about the BLAST telescope * '' A Blast'', a 2014 film directed by Syllas Tzoumerkas Magazines * ''Blast'' (magazine), a 1914–15 literary magazine of the Vorticist movement * ''Blast'' (U.S. magazine), a 1933–34 American short-story magazine * ''The Blast'' (magazine), a 1916–17 American anarchist periodical Music * Blast (American band), a hardcore punk band * Blast (Russian band), an indie band * ''Blast'' (album), by Holly Johnson, 1989 * ''The Blast'' (album), by Yuvan Shankar Raja, 1999 * " ...
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Approximate String Matching
In computer science, approximate string matching (often colloquially referred to as fuzzy string searching) is the technique of finding strings that match a pattern approximately (rather than exactly). The problem of approximate string matching is typically divided into two sub-problems: finding approximate substring matches inside a given string and finding dictionary strings that match the pattern approximately. Overview The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are: * insertion: ''cot'' → ''coat'' * deletion: ''coat'' → ''cot'' * substitution: ''coat'' → ''cost'' These three operations may be generalized as forms of substitution by adding a NULL character (here symbolized by *) wherever a character has been deleted or inserted: * insertion: ''co*t'' → ''coat'' * delet ...
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