Closed Sequential Pattern Mining Using Bi
... not only reduce the number of sequences presented to users but also increase the mining efficiency by pruning the enumeration space. Although mining closed subsequences shares a similar problem setting with mining closed itemsets [6, 10], the techniques developed in closed itemset mining cannot wor ...
... not only reduce the number of sequences presented to users but also increase the mining efficiency by pruning the enumeration space. Although mining closed subsequences shares a similar problem setting with mining closed itemsets [6, 10], the techniques developed in closed itemset mining cannot wor ...
Here - IEEE SSCI 2015
... Welcome Message from the President of the IEEE Computational Intelligence Society ...
... Welcome Message from the President of the IEEE Computational Intelligence Society ...
Find Potential Fraud Leads Using Data Mining Techniques
... comprehensive index, we use the PRINCOMP procedure in SAS to transform the observed ranks. The PRINCOMP procedure performs principal component analysis. Principal Components Analysis (PCA) is a technique used to reduce multidimensional data sets to lower dimensions for analysis. Its goal is to extra ...
... comprehensive index, we use the PRINCOMP procedure in SAS to transform the observed ranks. The PRINCOMP procedure performs principal component analysis. Principal Components Analysis (PCA) is a technique used to reduce multidimensional data sets to lower dimensions for analysis. Its goal is to extra ...
Data Mining the Genetics of Leukemia Geoff Morton
... data. In order to analyze these complex data we have developed a data-mining process that involves filtering the data to remove uninformative data attributes and then using a matrix-decomposition technique to cluster these data. We perform this datamining technique on the individual datasets as well ...
... data. In order to analyze these complex data we have developed a data-mining process that involves filtering the data to remove uninformative data attributes and then using a matrix-decomposition technique to cluster these data. We perform this datamining technique on the individual datasets as well ...
Outlier Detection Techniques
... • Mean and standard deviation are very sensitive to outliers • These values are computed for the complete data set (including potential outliers) • The MDist is used to determine outliers although the MDist values are influenced by these outliers => Minimum Covariance Determinant [Rousseeuw and Lero ...
... • Mean and standard deviation are very sensitive to outliers • These values are computed for the complete data set (including potential outliers) • The MDist is used to determine outliers although the MDist values are influenced by these outliers => Minimum Covariance Determinant [Rousseeuw and Lero ...
Slide - Department of Industrial Engineering
... Prepare Data for Analysis • Summarize: too much - no discriminant information too little - swamped with useless detail • Process for computer: EBCDIC, ASCII • Data encoding: how data are recorded can vary may have been collected with specific purpose (CAL omitting LA) • Textual data: avoid if possib ...
... Prepare Data for Analysis • Summarize: too much - no discriminant information too little - swamped with useless detail • Process for computer: EBCDIC, ASCII • Data encoding: how data are recorded can vary may have been collected with specific purpose (CAL omitting LA) • Textual data: avoid if possib ...
Big Data Clustering
... J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org ...
... J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org ...
A Novel Approach for Association Rule Mining using Pattern
... number of candidates and multiple scans of transaction database are the challenging issues for improvement of association rules algorithms. R. Agrawal, T. Imielinski and A. Swami [22] introduced AIS algorithm for association rule discovery over market basket data. It requires many passes over the da ...
... number of candidates and multiple scans of transaction database are the challenging issues for improvement of association rules algorithms. R. Agrawal, T. Imielinski and A. Swami [22] introduced AIS algorithm for association rule discovery over market basket data. It requires many passes over the da ...
H. Wang, H. Shan, A. Banerjee. Bayesian Cluster Ensembles
... recently proposed mixture modeling approach to learning cluster ensembles [1] is applicable to the variants, but the details have not been reported in the literature. In this paper, we propose Bayesian cluster ensembles (BCE), which can solve the basic cluster ensemble problem using a Bayesian appro ...
... recently proposed mixture modeling approach to learning cluster ensembles [1] is applicable to the variants, but the details have not been reported in the literature. In this paper, we propose Bayesian cluster ensembles (BCE), which can solve the basic cluster ensemble problem using a Bayesian appro ...
Data Mining Techniques for Mortality at Advanced Age
... Exponentiation the parameter estimate of each slope gives the odds ratio, which compares the odds of the event of one category to the odds of the event in another category. However, it poses several drawbacks especially when the size of the data getting large. The curse of dimensionality makes the d ...
... Exponentiation the parameter estimate of each slope gives the odds ratio, which compares the odds of the event of one category to the odds of the event in another category. However, it poses several drawbacks especially when the size of the data getting large. The curse of dimensionality makes the d ...
SEQUENTIAL PATTERN ANALYSIS IN DYNAMIC BUSINESS
... there are often multiple granularity levels accessible for estimating statistical models with the sequential data. However, on one hand, the patterns at the lowest level may be too complicated for the models to produce application-enabling results; and on the other hand, the patterns at the highest ...
... there are often multiple granularity levels accessible for estimating statistical models with the sequential data. However, on one hand, the patterns at the lowest level may be too complicated for the models to produce application-enabling results; and on the other hand, the patterns at the highest ...
Mining Surprising Patterns and Their Explanations in Clinical Data
... treatment. There are 96 variables that are categorized into 7 types: record tags, clinical attributes, clinical findings, procedure, disease/syndrome, behavior, and health care activity. The attributes in the dataset are either categorical or numerical, and some of the categorical attributes encode ...
... treatment. There are 96 variables that are categorized into 7 types: record tags, clinical attributes, clinical findings, procedure, disease/syndrome, behavior, and health care activity. The attributes in the dataset are either categorical or numerical, and some of the categorical attributes encode ...
evolving biologically inspired trading algorithms - BADA
... for optimal models in the solution space. This paper extends the HTM-based trading algorithm, developed in the previous work, by employing the genetic algorithm as an optimization method. Once again, neural networks are used as the benchmark technology since they are by far the most prevalent modeli ...
... for optimal models in the solution space. This paper extends the HTM-based trading algorithm, developed in the previous work, by employing the genetic algorithm as an optimization method. Once again, neural networks are used as the benchmark technology since they are by far the most prevalent modeli ...
ch2 - Personal Web Pages
... Clearly the space of all association rules is exponential, O(2m), where m is the number of items in I. The mining exploits sparseness of data, and high minimum support and high minimum confidence values. Still, it always produces a huge number of rules, thousands, tens of thousands, millions, ...
... Clearly the space of all association rules is exponential, O(2m), where m is the number of items in I. The mining exploits sparseness of data, and high minimum support and high minimum confidence values. Still, it always produces a huge number of rules, thousands, tens of thousands, millions, ...
An Overview of Web Data Clustering Practices
... patterns. The first step is to determine the attributes that should be used to estimate similarity between users’ sessions (in other words, we determine the users’ session representation). Then, it is determined the "strength" of the relationships between the attributes (similarity measures/correlat ...
... patterns. The first step is to determine the attributes that should be used to estimate similarity between users’ sessions (in other words, we determine the users’ session representation). Then, it is determined the "strength" of the relationships between the attributes (similarity measures/correlat ...
LNCS 3268 - An Overview of Web Data Clustering
... session patterns. The first step is to determine the attributes that should be used to estimate similarity between users’ sessions (in other words, we determine the users’ session representation). Then, it is determined the “strength” of the relationships between the attributes (similarity measures/ ...
... session patterns. The first step is to determine the attributes that should be used to estimate similarity between users’ sessions (in other words, we determine the users’ session representation). Then, it is determined the “strength” of the relationships between the attributes (similarity measures/ ...
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.