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Cluster Analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Cluster analysis itself is not one specific algorithm, but the general task to be solved. 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 distances between cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Discovery
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 criterion 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. The RDB2RDF W3C group is currently standardizing a language for extraction of resource description frameworks (RDF) from relational databases. Another popular example for knowledge extraction is the transformation of Wikipedia into structured data and also the mapping to existing knowledge ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DBSCAN
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996. It is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature. In 2014, the algorithm was awarded the test of time award (an award given to algorithms which have received substantial attention in theory and practice) at the leading data mining conference, ACM SIGKDD. , the follow-up paper "DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN" appears in the list of the 8 most downloaded articles of the prestigious ACM Transactions on Database Sys ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multivariate Normal Distribution
In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional ( univariate) normal distribution to higher dimensions. One definition is that a random vector is said to be ''k''-variate normally distributed if every linear combination of its ''k'' components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit theorem. The multivariate normal distribution is often used to describe, at least approximately, any set of (possibly) correlated real-valued random variables each of which clusters around a mean value. Definitions Notation and parameterization The multivariate normal distribution of a ''k''-dimensional random vector \mathbf = (X_1,\ldots,X_k)^ can be written in the following notation: : \mathbf\ \sim\ \mathcal(\boldsymbol\mu,\, \boldsymbol\Sigma), or to make it explicitly known that ''X'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-means Algorithm
''k''-means clustering is a method of vector quantization, originally from signal processing, that aims to partition ''n'' observations into ''k'' clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. ''k''-means clustering minimizes within-cluster variances ( squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids. The problem is computationally difficult (NP-hard); however, efficient heuristic algorithms converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hierarchical Clustering
In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: * Agglomerative: This is a " bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. * Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram. The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of \mathcal(n^3) and requires \Omega(n^2) memory, which makes it too slow for even medium data sets. However, for some special cases, optimal efficient agglomerative methods (of comp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Personality Psychology
Personality psychology is a branch of psychology that examines personality and its variation among individuals. It aims to show how people are individually different due to psychological forces. Its areas of focus include: * construction of a coherent picture of the individual and their major psychological processes * investigation of individual psychological differences * investigation of human nature and psychological similarities between individuals "Personality" is a dynamic and organized set of characteristics possessed by an individual that uniquely influences their environment, cognition, emotions, motivations, and behaviors in various situations. The word ''personality'' originates from the Latin ''persona'', which means " mask". Personality also pertains to the pattern of thoughts, feelings, social adjustments, and behaviors persistently exhibited over time that strongly influences one's expectations, self-perceptions, values, and attitudes. Personality also ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Raymond Cattell
Raymond Bernard Cattell (20 March 1905 – 2 February 1998) was a British-American psychologist, known for his psychometric research into intrapersonal psychological structure.Gillis, J. (2014). ''Psychology's Secret Genius: The Lives and Works of Raymond B. Cattell''. Amazon Kindle Edition. His work also explored the basic dimensions of personality and temperament, the range of cognitive abilities, the dynamic dimensions of motivation and emotion, the clinical dimensions of abnormal personality, patterns of group syntality and social behavior, applications of personality research to psychotherapy and learning theory,Cattell, R. B. (1987). ''Psychotherapy by Structured Learning Theory''. New York: Springer. predictors of creativity and achievement, and many multivariate research methodsCattell, R. B. (1966). (Ed.), ''Handbook of Multivariate Experimental Psychology''. Chicago, IL: Rand McNally. including the refinement of factor analytic methods for exploring and measuring these ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robert Tryon
Robert Choate Tryon (September 4, 1901 – September 27, 1967) was an American behavioral psychologist, who pioneered the study of hereditary trait inheritance and learning in animals. His series of experiments with laboratory rats showed that animals can be selectively bred for greater aptitude at certain intelligence tests, but that this selective breeding does not increase the general intelligence of the animals. Life Tryon was born in Butte, Montana on September 4, 1901. He spent most of his life at the University of California, Berkeley. He received his AB degree from the undergraduate school in 1924, and as a graduate student he earned his Ph.D. in 1928 with thesis titled ''Individual differences at successive stages of learning''. After graduating from the school he spent two years as a National Research Council fellow. In 1931, he became a faculty member of the college's Department of Psychology, of which he was a member for 31 years. During the War he served in Wa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Joseph Zubin
Joseph Zubin (9 October 1900 – 18 December 1990) was a Lithuanian-born American educational psychologist and an authority on schizophrenia who is commemorated by the Joseph Zubin Awards.''New York Times'' Dec 12, 1990 Dr. Joseph Zubin, 90, Research Psychologist Life Zubin was born October 9, 1900 in , , but moved to the US in 1908 and grew up in . His first degree was in |
Community Structure
In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. In the particular case of ''non-overlapping'' community finding, this implies that the network divides naturally into groups of nodes with dense connections internally and sparser connections between groups. But ''overlapping'' communities are also allowed. The more general definition is based on the principle that pairs of nodes are more likely to be connected if they are both members of the same community(ies), and less likely to be connected if they do not share communities. A related but different problem is community search, where the goal is to find a community that a certain vertex belongs to. Properties In the study of networks, such as computer and information networks, social networks and biological networks, a number of different charac ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
