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Relational Data Mining
Relational data mining is the data mining technique for relational databases.Dzeroski, Saso, Nada Lavrač, Lavrač, Nada (Eds.), Relational Data Mining, Springer 200/ref> Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most types of propositional patterns, there are corresponding relational patterns. For example, there are relational classification rules (relational classification), relational Decision tree learning, regression tree, and relational association rules. There are several approaches to relational data mining: # Inductive_logic_programming, Inductive Logic Programming (ILP) # Statistical_relational_learning, Statistical Relational Learning (SRL) # Structure mining, Graph Mining # Propositionalization # Multi-view learning Algorithms Multi-Relation Association Rules: Multi-Relation Association Rules (MRAR) is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Mining
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the " knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (''mining'') of data itself. It also is a buzzwo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nada Lavrač
Nada Lavrač (born 1953) is a Slovenian computer scientist, the former head of the Department of Knowledge Technologies of the Jožef Stefan Institute in Ljubljana. Her research has involved expert systems, logic programming, and rule induction in data mining, especially for applications in medicine. Education and career Lavrač studied technical mathematics at the University of Ljubljana, earning a bachelor's degree in 1978. She returned to the University of Ljubljana for a 1984 master's degree in computer science, and went to the University of Maribor for graduate study in technical sciences, completing her Ph.D. in 1990. Lavrač worked for ten years as a lecturer at the University of Klagenfurt in Austria, and for five years at the University of Bristol in England. She joined the Jožef Stefan Institute as a postdoctoral researcher in 1993. In 2004 she was named head of the Department of Knowledge Technologies, also becoming a full professor of the University of Ljubljana, t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Propositional Patterns
A proposition is a statement that can be either true or false. It is a central concept in the philosophy of language, semantics, logic, and related fields. Propositions are the object s denoted by declarative sentences; for example, "The sky is blue" expresses the proposition that the sky is blue. Unlike sentences, propositions are not linguistic expressions, so the English sentence "Snow is white" and the German "Schnee ist weiß" denote the same proposition. Propositions also serve as the objects of belief and other propositional attitudes, such as when someone believes that the sky is blue. Formally, propositions are often modeled as functions which map a possible world to a truth value. For instance, the proposition that the sky is blue can be modeled as a function which would return the truth value T if given the actual world as input, but would return F if given some alternate world where the sky is green. However, a number of alternative formalizations have been p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Relational Patterns
Relational may refer to: Business * Relational capital, the value inherent in a company's relationships with its customers, vendors, and other important constituencies * Relational contract, a contract whose effect is based upon a relationship of trust between the parties * Relational goods, goods that cannot be enjoyed alone * Relational Investors, an activist investment fund based in San Diego, California Computing * Relational calculus, part of the relational model for databases that provides a declarative way to specify database queries * Relational database, a database that has a collection of tables of data items, all of which is formally described and organized according to the relational model ** Relational classification, the procedure of performing classification in relational databases ** Relational data mining, the data mining technique for relational databases * Relational concept, a set of mathematically defined tuples in tuple relational calculus * Relational mo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Classification Rule
Given a population whose members each belong to one of a number of different sets or classes, a classification rule or classifier is a procedure by which the elements of the population set are each predicted to belong to one of the classes. A perfect classification is one for which every element in the population is assigned to the class it really belongs to. The bayes classifier is the classifier which assigns classes optimally based on the known attributes (i.e. features or regressors) of the elements to be classified. A special kind of classification rule is binary classification, for problems in which there are only two classes. Testing classification rules Given a data set consisting of pairs ''x'' and ''y'', where ''x'' denotes an element of the population and ''y'' the class it belongs to, a classification rule ''h''(''x'') is a function that assigns each element ''x'' to a predicted class \hat=h(x). A binary classification is such that the label ''y'' can take only one ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Tree Learning
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called Statistical classification, classification decision tree, trees; in these tree structures, leaf node, leaves represent class labels and branches represent Logical conjunction, conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression analysis, regression decision tree, trees. More generally, the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities such as categorical sequences. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Association Rule
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 buy ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Inductive Logic Programming
Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. The term "''inductive''" here refers to philosophical (i.e. suggesting a theory to explain observed facts) rather than mathematical (i.e. proving a property for all members of a well-ordered set) induction. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples. * Schema: ''positive examples'' + ''negative examples'' + ''background knowledge'' ⇒ ''hypothesis''. Inductive logic programming is particularly useful in bioinformatics and natural language processing. History Building on earlier work on Inductive inference, Gordon Plotkin was the first to formalise induction in a clausal setting around 1970, ad ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Relational Learning
Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s. As is evident from the characterization above, the field is not strictly limited to learning aspects; it is equally concerned with reasoning (specifically probabilistic inference) and knowledge representation. Therefore, alternative terms that reflect the main foci of the field ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Structure Mining
Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. Description The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applicatio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Mining
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the " knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (''mining'') of data itself. It also is a buzzwo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Database Mining
Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. Description The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applicatio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
