edge06
... Joint work with Alok Choudhary and Gokhan Memik (Northwestern) and Michael Steinbach (University of Minnesota) Research funded by NSF ...
... Joint work with Alok Choudhary and Gokhan Memik (Northwestern) and Michael Steinbach (University of Minnesota) Research funded by NSF ...
Wk1_lec - BASIS website
... of P often occur with high levels of K and N? …how often? …where? “Maps are numbers first, pictures later” Multivariate Analysis— each map layer is a continuous variable with all of the math/stat “rights, privileges and responsibilities” therewith …simply “spatially organized “ sets of numbers (matr ...
... of P often occur with high levels of K and N? …how often? …where? “Maps are numbers first, pictures later” Multivariate Analysis— each map layer is a continuous variable with all of the math/stat “rights, privileges and responsibilities” therewith …simply “spatially organized “ sets of numbers (matr ...
nvoss08 - Caltech Astronomy
... – Squared Error – Nearest Neighbor – K-Means (most popular) – Mixture Models (statistical) ...
... – Squared Error – Nearest Neighbor – K-Means (most popular) – Mixture Models (statistical) ...
Contrasting Xpriori Insight with Traditional Statistical Analysis, Data
... costly. Because of this, unknown information can remain hidden indefinitely. Xpriori Insight Although it uses algorithms, Xpriori Insight implements a heuristic analysis model. A heuristic is an incomplete set of guidelines with the potential for leading to greater learning and discovery. Insight al ...
... costly. Because of this, unknown information can remain hidden indefinitely. Xpriori Insight Although it uses algorithms, Xpriori Insight implements a heuristic analysis model. A heuristic is an incomplete set of guidelines with the potential for leading to greater learning and discovery. Insight al ...
extracting formations from long financial time series using data mining
... Previous attempts of solving the problem of mining predictive rules from time series can be classified into two main types. Supervised methods, where the targetrule form is known in advance and used as input to the rule extraction algorithm. Thus the objective of the analysis becomes to generate rul ...
... Previous attempts of solving the problem of mining predictive rules from time series can be classified into two main types. Supervised methods, where the targetrule form is known in advance and used as input to the rule extraction algorithm. Thus the objective of the analysis becomes to generate rul ...
Spatial Generalization and Aggregation of
... Abstract—Movement data (trajectories of moving agents) are hard to visualize: numerous intersections and overlapping between trajectories make the display heavily cluttered and illegible. It is necessary to use appropriate data abstraction methods. We suggest a method for spatial generalization and ...
... Abstract—Movement data (trajectories of moving agents) are hard to visualize: numerous intersections and overlapping between trajectories make the display heavily cluttered and illegible. It is necessary to use appropriate data abstraction methods. We suggest a method for spatial generalization and ...
k - E-Course - Πανεπιστήμιο Ιωαννίνων
... • The information content of the random variable X • The number of bits used for representing a value is the information content of this value. ...
... • The information content of the random variable X • The number of bits used for representing a value is the information content of this value. ...
HeteroClass: A Framework for Effective Classification
... it is inefficient and even impossible according to the following observations: • Studying heterogeneous databases has been an active research for long time. A key challenge of heterogeneous databases is how to deal with semantic heterogeneity presented by the multiple autonomous databases. Technique ...
... it is inefficient and even impossible according to the following observations: • Studying heterogeneous databases has been an active research for long time. A key challenge of heterogeneous databases is how to deal with semantic heterogeneity presented by the multiple autonomous databases. Technique ...
Bagging predictors | SpringerLink
... 77%. In Section 3 regression trees are bagged with reduction in test set mean squared error on data sets ranging from 21% to 46%. Section 4 goes over some theoretical justification for bagging and attempts to understand when it will or will not work well. This is illustrated by the results of Sectio ...
... 77%. In Section 3 regression trees are bagged with reduction in test set mean squared error on data sets ranging from 21% to 46%. Section 4 goes over some theoretical justification for bagging and attempts to understand when it will or will not work well. This is illustrated by the results of Sectio ...
Microsoft Business Intelligence on Oracle
... • ETL – SQL Server Integration Services • Data Warehouse – RDBMS: SQL Server or Oracle – OLAP and Data Mining: SQL Server Analysis Services – Alerting: SQL Server Notification Services ...
... • ETL – SQL Server Integration Services • Data Warehouse – RDBMS: SQL Server or Oracle – OLAP and Data Mining: SQL Server Analysis Services – Alerting: SQL Server Notification Services ...
Knowledge discovery from an ERP database in the context of new
... duration and cost. In each of these phases, the critical factors (parameters of an ERP database) that significantly influence new product development are sought. The estimation of these parameters is especially desired in the medium and large enterprises that develop a few new products simultaneous ...
... duration and cost. In each of these phases, the critical factors (parameters of an ERP database) that significantly influence new product development are sought. The estimation of these parameters is especially desired in the medium and large enterprises that develop a few new products simultaneous ...
Efficient Pattern Mining from Temporal Data through
... same range are represented by one symbol taken from an alphabet set. Basically, three types of periodic patterns [10] can be detected in a time series: 1) symbol periodicity, 2) sequence periodicity or partial periodic patterns, and 3) segment or full-cycle periodicity. A time series is said to have ...
... same range are represented by one symbol taken from an alphabet set. Basically, three types of periodic patterns [10] can be detected in a time series: 1) symbol periodicity, 2) sequence periodicity or partial periodic patterns, and 3) segment or full-cycle periodicity. A time series is said to have ...
CHOICE BASED CREDIT SYSTEM – STRUCTURE M.Sc
... To enable the students to gain a broad understanding of the discipline of software engineering and its application to the development and management of software systems. ...
... To enable the students to gain a broad understanding of the discipline of software engineering and its application to the development and management of software systems. ...
Data Mining and Predictive Modeling Workshop
... mailing cost, 2X response rate, 29% more profit” ...
... mailing cost, 2X response rate, 29% more profit” ...
Practical Approaches: A Survey on Data Mining Practical Tools
... that 19% of respondents are beyond the 50 gigabyte level, while 59% expect to be there by second quarter of 1996.1 In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel mu ...
... that 19% of respondents are beyond the 50 gigabyte level, while 59% expect to be there by second quarter of 1996.1 In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel mu ...
Temporal Sequence Classification in the Presence
... Depending on the representation of its events a sequence can be considered a Symbolic Sequence or a Time Series Sequence [18]. The main difference between them is that time series data is continuous whereas symbolic sequence data is discrete [1]. This distinction makes the nature of the distance fun ...
... Depending on the representation of its events a sequence can be considered a Symbolic Sequence or a Time Series Sequence [18]. The main difference between them is that time series data is continuous whereas symbolic sequence data is discrete [1]. This distinction makes the nature of the distance fun ...
Data mining application to decision-making processes in university
... 3.2 Cluster Analysis Once the relevant variables and categories, either latent or manifested, have been defined for the analysis, administrative procedures start being classified, grouping them in clusters through cluster analysis, based upon the scores of the variables employed [3]. This multivaria ...
... 3.2 Cluster Analysis Once the relevant variables and categories, either latent or manifested, have been defined for the analysis, administrative procedures start being classified, grouping them in clusters through cluster analysis, based upon the scores of the variables employed [3]. This multivaria ...
Understanding the indoor environment through mining sensory data
... 2.2. Data mining operations A data mining project was initially carried out in different ways with each data analyst based on his/her own experience and way of approaching the problem often through trial-and- error. Later, people introduced standardised data mining processes, among which two proces ...
... 2.2. Data mining operations A data mining project was initially carried out in different ways with each data analyst based on his/her own experience and way of approaching the problem often through trial-and- error. Later, people introduced standardised data mining processes, among which two proces ...
Data Mining using Conceptual Clustering
... property that conceptual clustering is mostly used for nominal-valued data. An extension exists for conceptual clustering that can deal with numeric data [2], but for the purpose of this paper we only need to be concerned with nominal-valued data as the data set we are dealing with is inherently nom ...
... property that conceptual clustering is mostly used for nominal-valued data. An extension exists for conceptual clustering that can deal with numeric data [2], but for the purpose of this paper we only need to be concerned with nominal-valued data as the data set we are dealing with is inherently nom ...
a subspace clustering of high dimensional data
... coverage. In such manners, the data space is first partitioned into a number of equal-sized units/grids [5]. The dense units whose densities exceed a predefined density threshold are identified, and finally, the groups of connected dense units are discovered as clusters. This paper we categorize suc ...
... coverage. In such manners, the data space is first partitioned into a number of equal-sized units/grids [5]. The dense units whose densities exceed a predefined density threshold are identified, and finally, the groups of connected dense units are discovered as clusters. This paper we categorize suc ...
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.