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Iris Flower Data Set
The ''Iris'' flower data set or Fisher's ''Iris'' data set is a Multivariate statistics, multivariate data set used and made famous by the British statistician and biologist Ronald Fisher in his 1936 paper ''The use of multiple measurements in taxonomic problems'' as an example of linear discriminant analysis. It is sometimes called Anderson's ''Iris'' data set because Edgar Anderson collected the data to quantify the morphology (biology), morphologic variation of ''Iris (plant), Iris'' flowers of three related species. Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus". The data set consists of 50 samples from each of three species of ''Iris'' (''Iris setosa'', ''Iris virginica'' and ''Iris versicolor''). Four Features (pattern recognition), features were measured from each sample: the length and the width of the sepals and petals, in centi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Iris Dataset Scatterplot
Iris most often refers to: *Iris (anatomy), part of the eye *Iris (color), an ambiguous color term *Iris (mythology), a Greek goddess *Iris (plant), ''Iris'' (plant), a genus of flowering plants * Iris (given name), a feminine given name, and a list of people so named Iris or IRIS may also refer to: Arts and media Fictional entities * Iris (American Horror Story), Iris (''American Horror Story''), an ''American Horror Story: Hotel'' character * Iris (Fire Force), Iris (''Fire Force''), a character in the manga series ''Fire Force'' * Iris (Mega Man), Iris (''Mega Man''), a ''Mega Man X4'' character ** Iris, a List of characters in the Mega Man Battle Network series#Iris, ''Mega Man Battle Network'' character * Iris (Pokémon), Iris (''Pokémon'') ** Iris (Pokémon anime), Iris (''Pokémon'' anime) * Sorceress Iris, a ''Magicians of Xanth'' character * Iris, a kaiju character in ''Gamera 3: The Revenge of Iris'' * Iris, a ''LoliRock'' character * Iris, a ''Lufia II: Rise of the S ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sepal
A sepal () is a part of the flower of angiosperms (flowering plants). Usually green, sepals typically function as protection for the flower in bud, and often as support for the petals when in bloom., p. 106 Etymology The term ''sepalum'' was coined by Noël Martin Joseph de Necker in 1790, and derived . Collectively, the sepals are called the ''calyx'' (plural: calyces), the outermost Whorl (botany), whorl of parts that form a flower. The word ''calyx'' was adopted from the Latin ,Jackson, Benjamin, Daydon; A Glossary of Botanic Terms with their Derivation and Accent; Published by Gerald Duckworth & Co. London, 4th ed 1928 not to be confused with 'cup, goblet'. The Latin ''calyx'' is derived from Greek 'bud, calyx, husk, wrapping' ( Sanskrit 'bud'), while is derived from Greek 'cup, goblet'; both words have been used interchangeably in botanical Latin. Description The term ''tepal'' is usually applied when the parts of the perianth are difficult to distinguish, e. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Principal Component Analysis
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified. The principal components of a collection of points in a real coordinate space are a sequence of p unit vectors, where the i-th vector is the direction of a line that best fits the data while being orthogonal to the first i-1 vectors. Here, a best-fitting line is defined as one that minimizes the average squared perpendicular distance from the points to the line. These directions (i.e., principal components) constitute an orthonormal basis in which different individual dimensions of the data are linearly uncorrelated. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pie Chart
A pie chart (or a circle chart) is a circular Statistical graphics, statistical graphic which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice (and consequently its central angle and area) is Proportionality (mathematics), proportional to the quantity it represents. While it is named for its resemblance to a pie which has been sliced, there are variations on the way it can be presented. The earliest known pie chart is generally credited to William Playfair's ''Statistical Breviary'' of 1801.Spence (2005)Tufte, p. 44 Pie charts are very widely used in the business world and the mass media.Cleveland, p. 262 However, they have been criticized,Wilkinson, p. 23. and many experts recommend avoiding them,Tufte, p. 178.van Belle, p. 160–162.Stephen Few"Save the Pies for Dessert" August 2007, Retrieved 2010-02-02Steve Fento"Pie Charts Are Bad"/ref> as research has shown it is more difficult to make simple comparisons such as the si ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SIGKDD
SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference. Conference history The KDD Conference grew from KDD (Knowledge Discovery and Data Mining) workshops at AAAI conferences, which were started by Gregory I. Piatetsky-Shapiro in 1989, 1991, and 1993, and Usama Fayyad in 1994. Conference papers of each proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining are published through ACM. KDD is widely considered the most influential forum for knowledge discovery and data mining research. The KDD conference has been held each year since 1995, and SIGKDD became an official ACM Special Interest Group in 1998. Past conference locations are listed on the KDD conference web site. The annual ACM SIGKDD conference is recognized as a flagship venue in the field. Based on statistics provided by independent researcher Lexing Xie in he ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Association For Computing Machinery
The Association for Computing Machinery (ACM) is a US-based international learned society for computing. It was founded in 1947 and is the world's largest scientific and educational computing society. The ACM is a non-profit professional membership group, reporting nearly 110,000 student and professional members . Its headquarters are in New York City. The ACM is an umbrella organization for academic and scholarly interests in computer science (informatics). Its motto is "Advancing Computing as a Science & Profession". History In 1947, a notice was sent to various people: On January 10, 1947, at the Symposium on Large-Scale Digital Calculating Machinery at the Harvard computation Laboratory, Professor Samuel H. Caldwell of Massachusetts Institute of Technology spoke of the need for an association of those interested in computing machinery, and of the need for communication between them. ..After making some inquiries during May and June, we believe there is ample interest to ... [...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] |
Cluster Analysis
Cluster analysis or clustering is the data analyzing technique in which task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more Similarity measure, similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistics, statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Cluster analysis refers to a family of algorithms and tasks rather than one specific algorithm. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small Distance function, distances between cluster members, dense areas of the data space, intervals or pa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Support Vector Machines
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). In addition to performing linear classification, SVMs can efficiently perform non-linear classification using the ''kernel trick'', representing the data only through a set of pairwise similarity comparisons between the original data points using a kernel function, which transforms them into coordinates in a higher-dimensional feature space. Thus, SVMs use the kernel trick to implicitly map their inputs into high-dimensional feature spaces, where linear classification can be performed. Being max-margin models, SVMs are resilient to noisy data (e.g., misclassified examples). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Classification
When classification is performed by a computer, statistical methods are normally used to develop the algorithm. Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or ''features''. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category. Terminology across fields is quite varied. In statistics, where classi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Principal Tree For Iris Data Set
Principal may refer to: Title or rank * Principal (academia), the chief executive of a university ** Principal (education), the head of a school * Principal (civil service) or principal officer, the senior management level in the UK Civil Service * Principal dancer, the top rank in ballet * Principal (music), the top rank in an orchestra Law * Principal (commercial law), the person who authorizes an agent ** Principal (architecture), licensed professional(s) with ownership of the firm * Principal (criminal law), the primary actor in a criminal offense * Principal (Catholic Church), an honorific used in the See of Lisbon Places * Principal, Cape Verde, a village * Principal, Ecuador, a parish Media * ''The Principal'' (TV series), a 2015 Australian drama series * ''The Principal'', a 1987 action film * Principal (music), the lead musician in a section of an orchestra * Principal photography, the first phase of movie production * "The Principal", a song on the album ''K-12'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
