Data Mining Image: Detail of sliced visualization of thirty video samples of Downfall remixes. See actual visualization below. As part of my post doctoral research for The Department of Information Science and Media Studies at the University of Bergen, Norway, I am using cultural analytics techniques to analyze YouTube video remixes. My research is done in collaboration with the Software Studies Lab at the University of California, San Diego. A big thank you to CRCA at Calit2 for providing a space for daily work during my stays in San Diego. The following is an excerpt from an upcoming paper titled, “Modular Complexity and Remix: The Collapse of Time and Space into Search,” to be published in the peer review journal AnthroVision, Vol 1.1. The following excerpt references sliced visualizations of the three cases studies in order to analyze the patterns of remixing videos on YouTube. Image: this is a slice visualization of “The Charleston and Lindy Hop Dance Remix.”
Data mining Process of extracting and discovering patterns in large data sets Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] 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.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] 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.[1] Etymology[edit] Background[edit] The manual extraction of patterns from data has occurred for centuries. Process[edit]
Eureqa Eureqa is a breakthrough technology that uncovers the intrinsic relationships hidden within complex data. Traditional machine learning techniques like neural networks and regression trees are capable tools for prediction, but become impractical when "solving the problem" involves understanding how you arrive at the answer. Eureqa uses a breakthrough machine learning technique called Symbolic Regression to unravel the intrinsic relationships in data and explain them as simple math. Over 35,000 people have relied on Eureqa to answer their most challenging questions, in industries ranging from Oil & Gas through Life Sciences and Big Box Retail. Eureqa One Page Overview (.pdf) »Visit the Eureqa Community » Eureqa utilizes a machine learning technique called Symbolic Regression to distill raw data into non-linear mathematical equations.
Relational data mining From Wikipedia, the free encyclopedia Relational data mining is the data mining technique for relational databases.[1] 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 regression tree, and relational association rules. There are several approaches to relational data mining: Multi-Relation Association Rules: Multi-Relation Association Rules (MRAR) is a new class of association rules which in contrast to primitive, simple and even multi-relational association rules (that are usually extracted from multi-relational databases), each rule item consists of one entity but several relations. Web page for a text book on relational data mining
GGobi data visualization system. Data mining - Simple English Wikipedia, the free encyclopedia Data mining is a term from computer science. Sometimes it is also called knowledge discovery in databases (KDD). Data mining is about finding new information in a lot of data. The information obtained from data mining is hopefully both new and useful. In many cases, data is stored so it can be used later. The data is saved with a goal. Later, the same data can also be used to get other information that was not needed for the first use. Finding new information that can also be useful from data, is called data mining. For data, there a lot of different kinds of data mining for getting new information. Pattern recognition (Trying to find similarities in the rows in the database, in the form of rules.
Graphviz Evolutionary data mining Process[edit] Data preparation[edit] Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned,[2] which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results.[3] At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset.[2] The training dataset will be used to let rules evolve which match it closely.[2] The test dataset will then either confirm or deny these rules.[2] Data mining[edit] This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible.[3] When this rule is obtained, it is then checked against the test dataset.[2] If the rule still matches the data, then the rule is valid and is kept.[2] If it does not match the data, then it is discarded and the process begins by selecting random rules again.[2]
Flare | Data Visualization for the Web Educational data mining Educational Data Mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings.[1] In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences.[2] The field is closely tied to that of learning analytics, and the two have been compared and contrasted.[3] Definition[edit] Educational Data Mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people's learning activities in educational settings. History[edit] Goals[edit]
FlowStone | Overview FlowStone uses a combination of graphical and text based programming. Applications are programmed by linking together functional building blocks called components. Events and data then flow between the links as the application executes. All this happens instantly - there's no compiling, your application runs as you build it making development an extremely rapid process. FlowStone allows you to create your own components using Ruby. This is a very modern language that is incredibly easy to pick up. The real power of FlowStone comes from modules. FlowStone can interface with a vast range of external hardware. This is one of the most powerful features of FlowStone. Once you are happy with your design running in the FlowStone environment you can simply click the export to EXE or VST buttons and your design will be wrapped up and made into a single, standalone program or plugin that you can run and distribute freely! You can try the software for seven days.
Oracle Data Mining Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics. It provides means for the creation, management and operational deployment of data mining models inside the database environment. Overview[edit] In data mining, the process of using a model to derive predictions or descriptions of behavior that is yet to occur is called "scoring". History[edit] Oracle Data Mining was first introduced in 2002 and its releases are named according to the corresponding Oracle database release: Oracle Data Mining 9iR2 (9.2.0.1.0 - May 2002)Oracle Data Mining 10gR1 (10.1.0.2.0 - February 2004)Oracle Data Mining 10gR2 (10.2.0.1.0 - July 2005)Oracle Data Mining 11gR1 (11.1 - September 2007)Oracle Data Mining 11gR2 (11.2 - September 2009) Functionality[edit] PL/SQL and Java interfaces[edit]
Protei >> 2013/04/25, 08:00 : Barcelona, Spain>> 2013/04/18, 08:00 - April 21, 20:00 : Casablanca, Morroco>> 2012/04/06, 08:00 - April 10, 20:00 Tema (Accra), Ghana>> 2013/03/25, 08:00 - March 30, 20:00 : CAPE TOWN, SOUTH AFRICA>> 2013/03/08, 08:00 - March 18, 20:00 : Port Louis, Mauritanie>> 2013/03/06 08:00 - March 11, 20:00 : Cochin, India>> 2013/03/01, 20:00 - Feb 25, 08:00 : Rangoon, Burma>> 2013/02/20, 08:00 - Feb 21, 20:00 : Singapore>> 2013/02/12, 08:00 - February 18, 16:00 : Ho Chi Minh, Vietnam>> 2013/02/07, 08:00 - Feb 8, 20:00 : Hong Kong>> 2013/02/03 : 08:00 - Feb 4, 20:00 : Shanghai, China>> 2013/01/30, 08:00 - Jan 31, 20:00 : Kobe, Japan>> 2013/01/27, 08:00 - Jan 28, 23:00 : Yokohama, Japan >> 2013/01/15, 08:00 - 16, 20:00 : Hilo, Hawaii, USA>> 2013/01/9, 17:00 : departure from San Diego, CA, USA >> 2012/11/29 : TEDxVilaMada "Nosso Planeta Agua" Sao Paolo, Brasil >> 2012/10/18 - 28 : Protei at Lodz Design Festival, Poland. Blog Origin of the name & Biomimicry Protei Community