4 Genetic Programming in Data Mining
... There are several properties of GP and genetic algorithms in general, which make them more convenient for application in DM comparing to the other techniques. One of them is their robustness and ability to work on large and “noisy” datasets. While most of the classification algorithms apply greedy s ...
... There are several properties of GP and genetic algorithms in general, which make them more convenient for application in DM comparing to the other techniques. One of them is their robustness and ability to work on large and “noisy” datasets. While most of the classification algorithms apply greedy s ...
Representing Unevenly-Spaced Time Series Data
... Our current research looks at online auction data, which consist of series of bids with timestamps, dollar amounts, bidder ID etc. Other examples are traffic incident data on highways, blood test results in patient records, and postings on Internet discussion boards. In these examples, the measureme ...
... Our current research looks at online auction data, which consist of series of bids with timestamps, dollar amounts, bidder ID etc. Other examples are traffic incident data on highways, blood test results in patient records, and postings on Internet discussion boards. In these examples, the measureme ...
comparative analysis of parallel k means and parallel fuzzy c means
... objects in the same cluster are as similar as possible and objects in different clusters are as dissimilar as possible. The equivalence classes induced by the clusters provide a means for generalizing over the data objects and their features. Clustering methods are applied in many domains, such as m ...
... objects in the same cluster are as similar as possible and objects in different clusters are as dissimilar as possible. The equivalence classes induced by the clusters provide a means for generalizing over the data objects and their features. Clustering methods are applied in many domains, such as m ...
Using Data Mining to Develop Profiles to Anticipate Attacks Systems
... Leaves no obvious traces associated with account used to access system. May leave Trojan horses behind for future access. Leaves virtually no useful evidence on the attached host ...
... Leaves no obvious traces associated with account used to access system. May leave Trojan horses behind for future access. Leaves virtually no useful evidence on the attached host ...
Data Warehouses
... supports decision making, business modeling, and operations research activities. Four Main Characteristics of OLAP ...
... supports decision making, business modeling, and operations research activities. Four Main Characteristics of OLAP ...
Data Mining Primitives, Languages, and System Architectures
... Represented as set of nodes organized in a tree Each node represents a concept All (represents the root). Most generalized value Consists of levels. Levels numbered top to bottom, with level 0 for all node. ...
... Represented as set of nodes organized in a tree Each node represents a concept All (represents the root). Most generalized value Consists of levels. Levels numbered top to bottom, with level 0 for all node. ...
Document
... Weka is a collection of machine learning algorithms for solving real-world data mining problems. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and v ...
... Weka is a collection of machine learning algorithms for solving real-world data mining problems. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and v ...
K355662
... Data Mining is one of the most significant tools for discovering association patterns that are useful for many knowledge domains. Yet, there are some drawbacks in existing mining techniques. The three main weaknesses of current data- mining techniques are: 1) rescanning of the entire database must b ...
... Data Mining is one of the most significant tools for discovering association patterns that are useful for many knowledge domains. Yet, there are some drawbacks in existing mining techniques. The three main weaknesses of current data- mining techniques are: 1) rescanning of the entire database must b ...
Test
... _____ Research shows that navigating a menu system with limited options is more accurate than a deeper one with many options per level. _____.Visual transitions in interactive displays help combat the change and inattentional blindness phenomena. _____.A data cube for data mining is created by multi ...
... _____ Research shows that navigating a menu system with limited options is more accurate than a deeper one with many options per level. _____.Visual transitions in interactive displays help combat the change and inattentional blindness phenomena. _____.A data cube for data mining is created by multi ...
EvalWare: Granular Computing for Web Applications
... RSES http://alfa.mimuw.edu.pl/~rses/ [analysis tool] The Rough Set Exploration System (RSES) is a tool for analysis of tabular data based on rough set theory. The tool can calculate reducts (i.e., sufficient sets of features), generate deci- ...
... RSES http://alfa.mimuw.edu.pl/~rses/ [analysis tool] The Rough Set Exploration System (RSES) is a tool for analysis of tabular data based on rough set theory. The tool can calculate reducts (i.e., sufficient sets of features), generate deci- ...
Data Mining
... mining in target marketing applications will increase from less than 5% to more than 80%. The META Group estimates that the data mining market will grow to $300 million by 1997 and to $800 million by the year 2000. However, the real promise of data mining is that software products will increasingly ...
... mining in target marketing applications will increase from less than 5% to more than 80%. The META Group estimates that the data mining market will grow to $300 million by 1997 and to $800 million by the year 2000. However, the real promise of data mining is that software products will increasingly ...
DTU: Decision Tree for Uncertain Data
... uncertain data. Decision tree is a commonly used data classification technique. Tree learning algorithms can generate decision tree models from a training data set. When working on uncertain data or probabilistic data, the learning and prediction algorithms need handle the uncertainty cautiously, or ...
... uncertain data. Decision tree is a commonly used data classification technique. Tree learning algorithms can generate decision tree models from a training data set. When working on uncertain data or probabilistic data, the learning and prediction algorithms need handle the uncertainty cautiously, or ...
Book Review: Domain Driven Data Mining
... multiple large heterogeneous data sources targeting more informative and actionable knowledge. The authors describe this approach as a framework for mining complex knowledge in complex data where many mutative applications can be designed such as combined pattern mining in multiple data sources. The ...
... multiple large heterogeneous data sources targeting more informative and actionable knowledge. The authors describe this approach as a framework for mining complex knowledge in complex data where many mutative applications can be designed such as combined pattern mining in multiple data sources. The ...
Mining Frequent Patterns Without Candidate Generation
... But strong correlations may still exist among a set of objects even if they are far apart from each other as measured by the distance function ...
... But strong correlations may still exist among a set of objects even if they are far apart from each other as measured by the distance function ...
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.