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Data mining

Data mining
Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.[1] It is an interdisciplinary subfield of computer science.[1][2][3] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it 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.[1] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[4] Etymology[edit] In the 1960s, statisticians used terms like "Data Fishing" or "Data Dredging" to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. (4) Modeling

https://en.wikipedia.org/wiki/Data_mining

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What Does It Mean to Think Historically? Introduction When we started working on Teachers for a New Era, a Carnegie-sponsored initiative designed to strengthen teacher training, we thought we knew a thing or two about our discipline. As we began reading such works as Sam Wineburg's Historical Thinking and Other Unnatural Acts, however, we encountered an unexpected challenge.1 If our understandings of the past constituted a sort of craft knowledge, how could we distill and communicate habits of mind we and our colleagues had developed through years of apprenticeship, guild membership, and daily practice to university students so that they, in turn, could impart these habits in K–12 classrooms? In response, we developed an approach we call the "five C's of historical thinking." The concepts of change over time, causality, context, complexity, and contingency, we believe, together describe the shared foundations of our discipline.

Data analysis Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. Knowledge extraction Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge (reusing identifiers or ontologies) or the generation of a schema based on the source data. Overview[edit]

Predictive analytics Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3] Why Study Digital History? teaching Posted by W. Caleb McDaniel on August 31, 2012 In our first meeting of Digital History at Rice, we each shared our reasons for wanting to study this subject.

Predictive analytics Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3] Grammar induction There is now a rich literature on learning different types of grammar and automata, under various different learning models and using various different methodologies. Grammar Classes[edit] Grammatical inference has often been very focused on the problem of learning finite state machines of various types (see the article Induction of regular languages for details on these approaches), since there have been efficient algorithms for this problem since the 1980s. More recently these approaches have been extended to the problem of inference of context-free grammars and richer formalisms, such as multiple context-free grammars and parallel multiple context-free grammars. Other classes of grammars for which grammatical inference has been studied are contextual grammars, and pattern languages. Learning Models[edit]

Business analytics Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.[1] Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling,[2] and fact-based management to drive decision making. It is therefore closely related to management science.

About Fusion Tables - Fusion Tables Help Bust your data out of its silo! Get more from data with Fusion Tables. Fusion Tables is an experimental data visualization web application to gather, visualize, and share data tables. Visualize bigger table data online Filter and summarize across hundreds of thousands of rows. These big data companies are ones to watch Which companies are breaking new ground with big data technology? We ask 10 industry experts. It’s hard enough staying on top of the latest developments in the technology industry. That’s doubly true in the fast-growing area known as big data, with new companies, products and services popping up practically every day. Online machine learning Online machine learning is used in the case where the data becomes available in a sequential fashion, in order to determine a mapping from the dataset to the corresponding labels. The key difference between online learning and batch learning (or "offline" learning) techniques, is that in online learning the mapping is updated after the arrival of every new datapoint in a scalable fashion, whereas batch techniques are used when one has access to the entire training dataset at once. Online learning could be used in the case of a process occurring in time, for example the value of a stock given its history and other external factors, in which case the mapping updates as time goes on and we get more and more samples. Ideally in online learning, the memory needed to store the function remains constant even with added datapoints, since the solution computed at one step is updated when a new datapoint becomes available, after which that datapoint can then be discarded. , where

Functional Database Model The functional database model is used to support analytics applications such as Financial Planning and Performance Management. The functional database model, or the functional model for short, is different from but complementary to, the relational model. The functional model is also distinct from other similarly named concepts, including the DAPLEX functional database model,[1] and functional language databases. The functional model is part of the online analytical processing (OLAP) category since it comprises multidimensional hierarchical consolidation.

Data Mining - PPDM Wiki From PPDM Wiki Introduction Traditional data analysis is done by inserting data into standards or customized models. In either case, it is assumed that the relationships among various system variables are well known and can be expressed mathematically. However, in many cases, relationships may not be known. Big Data 50 – the hottest Big Data startups of 2014 - Startup 50 From “Fast Data” to visualization software to tools used to track “Social Whales,” the Big Data 50 has it covered. The 50 startups in the Big Data 50 are an impressive lot. In fact, the Big Data space in general is so hot that you might start worrying about it overheating – kind of like one of those mid-summer drives through the Mojave Desert. The signs warn you to turn off your AC for a reason.

Wiki, but a great starting point for Data Mining -Josh by fritzjl Mar 28

Data mining, a branch of computer science,[1] is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. by agnesdelmotte Mar 24

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