Different Cube Computation Approaches: Survey Paper
... In many applications, like bioinformatics, statistics and text processing, datasets are characterized by high dimensionality e.g. over 100 dimensions -> 2100 cuboids in a full cube. As huge cube there is infeasible computation time. Iceberg cube is not an ultimate solution as it cannot be incrementa ...
... In many applications, like bioinformatics, statistics and text processing, datasets are characterized by high dimensionality e.g. over 100 dimensions -> 2100 cuboids in a full cube. As huge cube there is infeasible computation time. Iceberg cube is not an ultimate solution as it cannot be incrementa ...
Data Mining
... Origins of Data Mining Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems ...
... Origins of Data Mining Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems ...
The Linked Data Mining Challenge 2015
... find for each attribute associated with a movie, on average how many times that attribute is associated with a “good” or “bad” movie. Then, the value can be used to surmise with some degree of certainty that the score as determined by taking the average of all attributes will be a good indicator of ...
... find for each attribute associated with a movie, on average how many times that attribute is associated with a “good” or “bad” movie. Then, the value can be used to surmise with some degree of certainty that the score as determined by taking the average of all attributes will be a good indicator of ...
Evaluation of Student Performance Using Data Mining Over a Given
... in the data that are in some way of “similar”, without using known structure in the data. The goal is to partition a set of records into groups such that records within a group are similar to each other and records that belong to two different groups are dissimilar. Similarity between records is mea ...
... in the data that are in some way of “similar”, without using known structure in the data. The goal is to partition a set of records into groups such that records within a group are similar to each other and records that belong to two different groups are dissimilar. Similarity between records is mea ...
Data Mining And Warehousing
... The value from each hidden node is a function of the weighted sum of the values from all the preceding nodes that feed into it. The process of building a model involves finding the connection weights that produce the most accurate results by "training" the neural net with data. The most common train ...
... The value from each hidden node is a function of the weighted sum of the values from all the preceding nodes that feed into it. The process of building a model involves finding the connection weights that produce the most accurate results by "training" the neural net with data. The most common train ...
K-means Clustering - University of Minnesota
... The K-means algorithm assumes that it is meaningful to take the mean (average) of a set of data objects. ...
... The K-means algorithm assumes that it is meaningful to take the mean (average) of a set of data objects. ...
Data mining as a tool for Customer Selection
... * Claims ratio for Non Life in 1997 : 84,7 Cost ratio for Non Life in 1997 : 25,8 This yields a combined ratio of 110,5. Due to financial income…still good results... * In 1996 Gjensidige had a net increase of almost 16000 private cars. * Gjensidige`s agreement with the Norwegian Automobile Associat ...
... * Claims ratio for Non Life in 1997 : 84,7 Cost ratio for Non Life in 1997 : 25,8 This yields a combined ratio of 110,5. Due to financial income…still good results... * In 1996 Gjensidige had a net increase of almost 16000 private cars. * Gjensidige`s agreement with the Norwegian Automobile Associat ...
PPT
... The key principle for effective sampling is the following: – using a sample will work almost as well as using the entire data sets, if the sample is representative – A sample is representative if it has approximately the same property (of interest) as the original set of data ...
... The key principle for effective sampling is the following: – using a sample will work almost as well as using the entire data sets, if the sample is representative – A sample is representative if it has approximately the same property (of interest) as the original set of data ...
Models and Algorithms for High-Performance Distributed Data Mining
... Computing). Emerging application scenarios like Social Networks play as well the role of interesting contexts that may stimulate further investigation in this field. In Distributed Data Mining models and algorithms, high-performance is not only an architecture-and-resource--oriented matter, but also ...
... Computing). Emerging application scenarios like Social Networks play as well the role of interesting contexts that may stimulate further investigation in this field. In Distributed Data Mining models and algorithms, high-performance is not only an architecture-and-resource--oriented matter, but also ...
Open-Source Tools for Data Mining in Social Science
... Data mining can be defined as the application of machine learning algorithms (Mitchell, 1997) for semiautomatic or automatic extraction of information from data stored in databases (Chakrabarti et al., 2009; Witten et al., 2011). The goal of data mining is to extract knowledge from the data set in h ...
... Data mining can be defined as the application of machine learning algorithms (Mitchell, 1997) for semiautomatic or automatic extraction of information from data stored in databases (Chakrabarti et al., 2009; Witten et al., 2011). The goal of data mining is to extract knowledge from the data set in h ...
401(k) DSS
... • Develop a 401K DSS that shows an employee how different contribution amounts will affect their retirement savings over the next five to 30 years. • The DSS must be user-friendly because most employees are not familiar with the spreadsheet software like Excel. • The DSS must be able to prevent a us ...
... • Develop a 401K DSS that shows an employee how different contribution amounts will affect their retirement savings over the next five to 30 years. • The DSS must be user-friendly because most employees are not familiar with the spreadsheet software like Excel. • The DSS must be able to prevent a us ...
Chapter Ten
... • Analyze if sufficient data is available to obtain statistically significant results • If enough data is available, split the data into two samples: – the train set to fit the models ...
... • Analyze if sufficient data is available to obtain statistically significant results • If enough data is available, split the data into two samples: – the train set to fit the models ...
Data mining in the e
... are formed so that objects within a cluster have high similarity in comparison with one another, but are very dissimilar to objects in other clusters. Each cluster can be viewed as a class of objects, from which rules can be derived (Dunham, 2003, p. 203). ...
... are formed so that objects within a cluster have high similarity in comparison with one another, but are very dissimilar to objects in other clusters. Each cluster can be viewed as a class of objects, from which rules can be derived (Dunham, 2003, p. 203). ...
Concept-adapting Very Fast Decision Tree with
... call as supervised learning otherwise unsupervised learning. If data available for learning is high dimensional data or data contains the data of irrelevant feature then there is need to remove those irrelevant features, feature selection is used to detect the irrelevant features. Classification is ...
... call as supervised learning otherwise unsupervised learning. If data available for learning is high dimensional data or data contains the data of irrelevant feature then there is need to remove those irrelevant features, feature selection is used to detect the irrelevant features. Classification is ...
CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE
... a. Association rule: Association rules can be classified in various ways, based on the following criteria ● Based on the types of values handled in the rule ● Based on the dimensions of data involved in the rule ● Based on the levels of abstractions involved in the rule set ● Based on various extens ...
... a. Association rule: Association rules can be classified in various ways, based on the following criteria ● Based on the types of values handled in the rule ● Based on the dimensions of data involved in the rule ● Based on the levels of abstractions involved in the rule set ● Based on various extens ...
Data - Università degli Studi di Milano
... 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 converse is not true: ...
... 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 converse is not true: ...
Heart disease classification and its co
... have used several features to diagnosis the disease, which has been collected from patients. By applying different data mining algorithms, the patient data is taken for the experiment. The main drawbacks of the previous studies are that need accurate and more number of features. In this paper, a Dat ...
... have used several features to diagnosis the disease, which has been collected from patients. By applying different data mining algorithms, the patient data is taken for the experiment. The main drawbacks of the previous studies are that need accurate and more number of features. In this paper, a Dat ...
Chapter12
... • The resulting structure is called an alternating decision tree, with splitter nodes and prediction nodes • Prediction nodes are leaf nodes if no splitter nodes have been added to them yet • Standard alternating tree applies to 2-class problems but the algorithm can be extended to multi-class probl ...
... • The resulting structure is called an alternating decision tree, with splitter nodes and prediction nodes • Prediction nodes are leaf nodes if no splitter nodes have been added to them yet • Standard alternating tree applies to 2-class problems but the algorithm can be extended to multi-class probl ...
Discovery Net
... Specialised Client Interfaces: Interactive Analysis and dynamic component interaction ...
... Specialised Client Interfaces: Interactive Analysis and dynamic component interaction ...
Applications of Data Mining to Electronic Commerce
... mining, rather than having to struggle with translating and mining data collected for other purposes. Data are collected electronically, rather than manually, so less noise is introduced from manual processing. Electronic commerce data are rich, containing information on prior purchase activity and ...
... mining, rather than having to struggle with translating and mining data collected for other purposes. Data are collected electronically, rather than manually, so less noise is introduced from manual processing. Electronic commerce data are rich, containing information on prior purchase activity and ...
a review on machine learning in data mining
... sustainability of internet. For this a number of spam detection algorithms have been proposed, but they were not up to the mark. With machine learning algorithms the data analysis is carried out by classifying spam words as an identification mark. These patterns are unique and close patterns which a ...
... sustainability of internet. For this a number of spam detection algorithms have been proposed, but they were not up to the mark. With machine learning algorithms the data analysis is carried out by classifying spam words as an identification mark. These patterns are unique and close patterns which a ...
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.