3.Data transformation
... Discrete wavelet transform (DWT): linear signal processing Compressed approximation: store only a small fraction of the strongest of the wavelet ...
... Discrete wavelet transform (DWT): linear signal processing Compressed approximation: store only a small fraction of the strongest of the wavelet ...
Data Mining
... example in table 1 as the database to be mined: Summarization. It aims at producing compact and characteristic descriptions for a given set of data. It can take multiple forms: numerical (simple descriptive statistical measures like means, standard deviations…), graphical forms (histograms, scatter ...
... example in table 1 as the database to be mined: Summarization. It aims at producing compact and characteristic descriptions for a given set of data. It can take multiple forms: numerical (simple descriptive statistical measures like means, standard deviations…), graphical forms (histograms, scatter ...
Ant Colony Systems Data Mining
... E = {Oi,…, On} Set of n data or objects collected. Oi = {vi,…, vk} Each object is a vector of k numerical attributes. ...
... E = {Oi,…, On} Set of n data or objects collected. Oi = {vi,…, vk} Each object is a vector of k numerical attributes. ...
Trajectory Boundary Modeling of Time Series for Anomaly Detection
... inversely related to P(y). Ypma [15] surveys some important techniques, such as Bayesian models, neural networks, and support vector machines, and applications to the detection of failures in rotating machinery using vibration sensors ...
... inversely related to P(y). Ypma [15] surveys some important techniques, such as Bayesian models, neural networks, and support vector machines, and applications to the detection of failures in rotating machinery using vibration sensors ...
Knowledge Discovery in Databases
... KDD activities. “Data warehousing is a process, not a product, for assembling and managing data from various sources for the purpose of gaining a single, detailed view of part or all of a business” (Gardner, 1998, p. 54). If data warehousing is undertaken in a planned and logical manner, according t ...
... KDD activities. “Data warehousing is a process, not a product, for assembling and managing data from various sources for the purpose of gaining a single, detailed view of part or all of a business” (Gardner, 1998, p. 54). If data warehousing is undertaken in a planned and logical manner, according t ...
detecting malicious use with unlabelled data using clustering and
... Ak is an approximation of the matrix A. This decomposition does not reproduce A exactly even if k = n, but uses very little storage with respect to the observed accuracy of the approximation [Kol97]. The matrix A represents a dataset with m rows representing records, and n columns representing attri ...
... Ak is an approximation of the matrix A. This decomposition does not reproduce A exactly even if k = n, but uses very little storage with respect to the observed accuracy of the approximation [Kol97]. The matrix A represents a dataset with m rows representing records, and n columns representing attri ...
Data Warehousing and Data Mining Unit 1 and 2
... Data Cube Measures: Three Categories Distributive: if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning E.g., count(), sum(), min(), max() Algebraic: if it can be computed by an algebraic ...
... Data Cube Measures: Three Categories Distributive: if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning E.g., count(), sum(), min(), max() Algebraic: if it can be computed by an algebraic ...
Tadesse_poster - Southeast Regional Climate Center
... VegOut maps are produced using rule-based regression tree models that were generated to identify similar historical relationships (patterns) in space and time between satellite-derived vegetation conditions, climate-based drought indices, oceanic indices, and biophysical data. The data used to produ ...
... VegOut maps are produced using rule-based regression tree models that were generated to identify similar historical relationships (patterns) in space and time between satellite-derived vegetation conditions, climate-based drought indices, oceanic indices, and biophysical data. The data used to produ ...
Data Mining?
... Compare: C’k+1 = Ck Ck Ck+1 = Lk Lk Note that C’k+1 Ck+1 This variation can pay off in later passes, when the cost of counting and keeping in memory additional C’k+1 - Ck+1 candidates becomes less than the cost of scanning the database; There has to be enough space in main memory for both Ck a ...
... Compare: C’k+1 = Ck Ck Ck+1 = Lk Lk Note that C’k+1 Ck+1 This variation can pay off in later passes, when the cost of counting and keeping in memory additional C’k+1 - Ck+1 candidates becomes less than the cost of scanning the database; There has to be enough space in main memory for both Ck a ...
Instant Selection of High Contrast Projections in Multi
... subspaces for each time point. The selection and order of this ranking is based on the contrast function contrast : P(DIM ) → R. It provides the contrast of a subset of the dimensions. Please note that for processing reasons we base on a window-based computation of the contrast. As processing unit w ...
... subspaces for each time point. The selection and order of this ranking is based on the contrast function contrast : P(DIM ) → R. It provides the contrast of a subset of the dimensions. Please note that for processing reasons we base on a window-based computation of the contrast. As processing unit w ...
Integrating E-Commerce and Data Mining: Architecture and
... plentiful, electronic collection provides reliable data, in sight can easily be turned into action, and return on in vestment can be measured. To really take advantage of this domain, however, data mining must be integrated into the e-commerce systems with the appropriate data transformation bridges ...
... plentiful, electronic collection provides reliable data, in sight can easily be turned into action, and return on in vestment can be measured. To really take advantage of this domain, however, data mining must be integrated into the e-commerce systems with the appropriate data transformation bridges ...
Spatio-Temporal Outlier Detection in Precipitation Data
... more accurately could enhance our understanding of many different application areas. One such application area is in the field of Hydrology, where knowledge about the behaviour of unusual precipitation could allow governments and individuals to better prepare for extreme events such as floods. Perfo ...
... more accurately could enhance our understanding of many different application areas. One such application area is in the field of Hydrology, where knowledge about the behaviour of unusual precipitation could allow governments and individuals to better prepare for extreme events such as floods. Perfo ...
OPTICS: Ordering Points To Identify the Clustering Structure
... is basedon grid cell densities [JD 881.A histogram is constructed by partitioning the data space into a number of non-overlalpping regions or cells. Cells containing a relatively large number of objects are potential cluster centers and the boundaries between clusters fall in the “valleys” of the hi ...
... is basedon grid cell densities [JD 881.A histogram is constructed by partitioning the data space into a number of non-overlalpping regions or cells. Cells containing a relatively large number of objects are potential cluster centers and the boundaries between clusters fall in the “valleys” of the hi ...
Self-organizing learning array and its application to economic and
... boosting [12], which is designed to boost the accuracy of individual learning algorithms. In many approaches, artificial neural networks (ANNs) were used for data mining and knowledge extraction in large data sets. An ANN processes information by simulating biological neural systems and has been one ...
... boosting [12], which is designed to boost the accuracy of individual learning algorithms. In many approaches, artificial neural networks (ANNs) were used for data mining and knowledge extraction in large data sets. An ANN processes information by simulating biological neural systems and has been one ...
Classification Algorithms of Data Mining
... Bayesian classifiers are also useful in that they provide a theoretical justification for other classifiers that do not explicitly use Bayes’ theorem. For example, under certain assumptions, it can be shown that many neural network and curve-fitting algorithms output the maximum posteriori hypothesi ...
... Bayesian classifiers are also useful in that they provide a theoretical justification for other classifiers that do not explicitly use Bayes’ theorem. For example, under certain assumptions, it can be shown that many neural network and curve-fitting algorithms output the maximum posteriori hypothesi ...
Integrating Data Mining and Agent Based Modeling and Simulation
... Applying DM in ABMS aiming to provide solutions to the open problem (further described in section 2.2) in ABMS investigation, based on DM techniques; and ...
... Applying DM in ABMS aiming to provide solutions to the open problem (further described in section 2.2) in ABMS investigation, based on DM techniques; and ...
Data Mining
... Intelligence (COSC 6368), and Machine Learning (COSC 6342). Moreover, having basic knowledge in data structures, software design, and databases is important when conducting data mining projects; therefore, taking COSC 6320, COSC 6318 and COSC 6340 is a good choice. Moreover, taking a course that tea ...
... Intelligence (COSC 6368), and Machine Learning (COSC 6342). Moreover, having basic knowledge in data structures, software design, and databases is important when conducting data mining projects; therefore, taking COSC 6320, COSC 6318 and COSC 6340 is a good choice. Moreover, taking a course that tea ...
methodologies of knowledge discovery from data and data mining
... • DM methods allow rapidly obtaining the (often hidden) knowledge about analyzed products, processes and phenomena [23]. • They assist in decision making, for example in preparing prognoses and detection of frauds [24]. • They do not require performing very expensive experiments – they are based on ...
... • DM methods allow rapidly obtaining the (often hidden) knowledge about analyzed products, processes and phenomena [23]. • They assist in decision making, for example in preparing prognoses and detection of frauds [24]. • They do not require performing very expensive experiments – they are based on ...
A Multi-Resolution Clustering Approach for Very Large Spatial
... CLARA (Clustering LARge Applications) [KR90] draws a sample of data set, applies PAM on the sample, and nds the medoids of the sample. Ng and Han introduced CLARANS (Clustering Large Applications based on RANdomaized Search) which is an improved k-medoid method [NH94]. This is the rst method that ...
... CLARA (Clustering LARge Applications) [KR90] draws a sample of data set, applies PAM on the sample, and nds the medoids of the sample. Ng and Han introduced CLARANS (Clustering Large Applications based on RANdomaized Search) which is an improved k-medoid method [NH94]. This is the rst method that ...
Frequent Closures as a Concise Representation for Binary Data
... An explicit interestingness evaluation of all the patterns of P R in a dataset is not tractable in general. Though an exponential search space is concerned, frequent sets can be computed in real-life large datasets thanks to the support threshold on one hand and safe pruning criteria that drasticall ...
... An explicit interestingness evaluation of all the patterns of P R in a dataset is not tractable in general. Though an exponential search space is concerned, frequent sets can be computed in real-life large datasets thanks to the support threshold on one hand and safe pruning criteria that drasticall ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.