Big Data
... • How to store and protect big data? • How to backup and restore big data? • How to organize and catalog the data that you have backed up? • How to keep costs low while ensuring that all the critical data is available when you need it? ...
... • How to store and protect big data? • How to backup and restore big data? • How to organize and catalog the data that you have backed up? • How to keep costs low while ensuring that all the critical data is available when you need it? ...
A Preview on Subspace Clustering of High Dimensional Data
... correlations among them. (v) The wrong selection of a proximity measure used by a clustering technique may lead to the discovery of some similar groups of genes at the expense of obscuring other similar groups. Feature selection methods have been employed somewhat successfully to improve cluster qua ...
... correlations among them. (v) The wrong selection of a proximity measure used by a clustering technique may lead to the discovery of some similar groups of genes at the expense of obscuring other similar groups. Feature selection methods have been employed somewhat successfully to improve cluster qua ...
performance comparison of time series data using predictive data
... In a neural network, PEs can be interconnected in various ways. Typically, PEs are structured into layers and the output values of PEs in one layer serve as input values for PEs in the next layer. Each connection has a weight associated with it. In most cases, a Processing Element calculates a weigh ...
... In a neural network, PEs can be interconnected in various ways. Typically, PEs are structured into layers and the output values of PEs in one layer serve as input values for PEs in the next layer. Each connection has a weight associated with it. In most cases, a Processing Element calculates a weigh ...
Secure Semantic Computing
... This example also illustrates that secure ontology alignment can offer rare and valuable opportunities to automatically detect and correct policy specification errors through the detection of policy conflicts. Data security policy specifications for massive repositories are often highly complex and ...
... This example also illustrates that secure ontology alignment can offer rare and valuable opportunities to automatically detect and correct policy specification errors through the detection of policy conflicts. Data security policy specifications for massive repositories are often highly complex and ...
Knowledge Discovery in Data with FIT-Miner
... existing columns. A target column can be a numeric or a categorical attribute represented by string value. This function can be useful when the new attribute can be derived from several existing attributes. This trasformation is useful for dimensionality reduction. These user-defined functions are s ...
... existing columns. A target column can be a numeric or a categorical attribute represented by string value. This function can be useful when the new attribute can be derived from several existing attributes. This trasformation is useful for dimensionality reduction. These user-defined functions are s ...
A Data-Driven Paradigm to Understand Multimodal Communication
... in machine learning and data mining provide tools for the discovery of only predefined structures in complex heterogeneous time series (hidden Markov models, dynamic time warping, Markov random fields, to name a few, e.g. [6]). A relevant research field to knowledge discovery is information visualiz ...
... in machine learning and data mining provide tools for the discovery of only predefined structures in complex heterogeneous time series (hidden Markov models, dynamic time warping, Markov random fields, to name a few, e.g. [6]). A relevant research field to knowledge discovery is information visualiz ...
Chapter 3 slides
... Positive covariance: If CovA,B > 0, then if A is larger than its expected value, B is also likely to be larger than its expected value. Negative covariance: If CovA,B < 0 then if A is larger than its expected value, B is likely to be smaller than its expected value. Independence: CovA,B = 0 but the ...
... Positive covariance: If CovA,B > 0, then if A is larger than its expected value, B is also likely to be larger than its expected value. Negative covariance: If CovA,B < 0 then if A is larger than its expected value, B is likely to be smaller than its expected value. Independence: CovA,B = 0 but the ...
Clustering
... • A good clustering method will produce high quality clusters with – high intra-class similarity – low inter-class similarity ...
... • A good clustering method will produce high quality clusters with – high intra-class similarity – low inter-class similarity ...
The Coming Big Data Tsunami in Energy Market Analytics
... ○ data starts to become big when you can’t make use of the raw data without summarizing it ● Three types of analytics 1. Descriptive 2. Predictive 3. Prescriptive ...
... ○ data starts to become big when you can’t make use of the raw data without summarizing it ● Three types of analytics 1. Descriptive 2. Predictive 3. Prescriptive ...
Business Understanding Example Business objectives Assess
... of previously solved similar problems in solving the new problem ...
... of previously solved similar problems in solving the new problem ...
WK01311891199
... However, if the clusters are close to one another (even by outliers), or if their shapes and sizes are not hyperspherical and uniform, the results of clustering can vary quite dramatically. For example, with the data set shown in Figure l(a), using d,,,, d,,, or d,,,, as the distance measure results ...
... However, if the clusters are close to one another (even by outliers), or if their shapes and sizes are not hyperspherical and uniform, the results of clustering can vary quite dramatically. For example, with the data set shown in Figure l(a), using d,,,, d,,, or d,,,, as the distance measure results ...
What Is Data Mining? Data Mining
... ● Neural Network-a computer system modeled on the human brain and nervous system. ● These systems are self-learning and trained, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. ...
... ● Neural Network-a computer system modeled on the human brain and nervous system. ● These systems are self-learning and trained, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. ...
Lec14DataMining
... Descriptive model that discovers sequence correlations in timesequenced data. For example, ‘People who have purchased a VCR are 300% more likely to purchase a camcorder in the time period 2-4 months after the VCR was purchased’ ...
... Descriptive model that discovers sequence correlations in timesequenced data. For example, ‘People who have purchased a VCR are 300% more likely to purchase a camcorder in the time period 2-4 months after the VCR was purchased’ ...
Lec14DataMining
... Descriptive model that discovers sequence correlations in timesequenced data. For example, ‘People who have purchased a VCR are 300% more likely to purchase a camcorder in the time period 2-4 months after the VCR was purchased’ ...
... Descriptive model that discovers sequence correlations in timesequenced data. For example, ‘People who have purchased a VCR are 300% more likely to purchase a camcorder in the time period 2-4 months after the VCR was purchased’ ...
A Lattice Algorithm for Data Mining
... When generating concepts, lattice algorithm focusses on objects or attributes. So if the number of objects is greater than the number of attributes, it might be interesting to build the concept node based on the minimum number between objects and attributes [FU 03a, RIO 03]. We propose a new definit ...
... When generating concepts, lattice algorithm focusses on objects or attributes. So if the number of objects is greater than the number of attributes, it might be interesting to build the concept node based on the minimum number between objects and attributes [FU 03a, RIO 03]. We propose a new definit ...
Software Defect Prediction Using Regression via Classification
... the fault class of a software system. Finally, RvC transforms the class output of the model back into a numeric prediction. This approach includes uncertainty in the models because apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lie ...
... the fault class of a software system. Finally, RvC transforms the class output of the model back into a numeric prediction. This approach includes uncertainty in the models because apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lie ...
frequent correlated periodic pattern mining for large volume set
... The traditional association periodic pattern mining problem is well defined and has been thoroughly studied in last decade (Elfeky et al., 2005a; Rasheed et al., 2011), there is currently no canonical way to measure the degree of correlation between periodic patterns (Huang and Chang, 2005). We beli ...
... The traditional association periodic pattern mining problem is well defined and has been thoroughly studied in last decade (Elfeky et al., 2005a; Rasheed et al., 2011), there is currently no canonical way to measure the degree of correlation between periodic patterns (Huang and Chang, 2005). We beli ...
Sequential Pattern Mining in Multiple Streams
... set Mult1 has one stream containing a token (with a probability of 0.55) that happens more frequently than others (each of which is associated with a probability of 0.15); (2) data set Mult2 has two streams each of which contains a token (with a probability of 0.55) that happens more frequently than ...
... set Mult1 has one stream containing a token (with a probability of 0.55) that happens more frequently than others (each of which is associated with a probability of 0.15); (2) data set Mult2 has two streams each of which contains a token (with a probability of 0.55) that happens more frequently than ...
Data Warehouse
... Provide efficient implement a few data mining primitives in a DB/DW system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of ...
... Provide efficient implement a few data mining primitives in a DB/DW system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of ...
A Collaborative Approach of Frequent Item Set Mining
... A memory-based, efficient pattern-growth algorithm, Hmine is for mining frequent patterns for the datasets that can fit in memory [4]. A simple, memory-based hyperstructure, H-struct, is planned for fast mining. H-mine has polynomial space complexity and is thus more space efficient than pattern-gro ...
... A memory-based, efficient pattern-growth algorithm, Hmine is for mining frequent patterns for the datasets that can fit in memory [4]. A simple, memory-based hyperstructure, H-struct, is planned for fast mining. H-mine has polynomial space complexity and is thus more space efficient than pattern-gro ...
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.