IOSR Journal of Computer Engineering (IOSR-JCE)
... similarity and item-side similarity at the same time which shows that the similarity learning problems can be formulated as problems of matrix factorization with missing values. Second contribution is collection of algorithms that use the learned similarity functions for CF, which is known as simila ...
... similarity and item-side similarity at the same time which shows that the similarity learning problems can be formulated as problems of matrix factorization with missing values. Second contribution is collection of algorithms that use the learned similarity functions for CF, which is known as simila ...
Scalable and interpretable data representation for high
... The work is part of an effort to provide analysis reports for those who use ML as a problem-solving tool and require an efficient way to interpret, evaluate and debug ML problems. The report provides information about individual data points and their relationships with one another and with class lab ...
... The work is part of an effort to provide analysis reports for those who use ML as a problem-solving tool and require an efficient way to interpret, evaluate and debug ML problems. The report provides information about individual data points and their relationships with one another and with class lab ...
Exploring Linked Data Graph Structures - CEUR
... begin the analysis. A number of several interesting datasets from various domains have been already imported, including DBpedia, Diseasome, and LinkedMDB. After the initial analysis phase, users can select datasets and clusters in a tree model and browse the profiling results across several tabs. Th ...
... begin the analysis. A number of several interesting datasets from various domains have been already imported, including DBpedia, Diseasome, and LinkedMDB. After the initial analysis phase, users can select datasets and clusters in a tree model and browse the profiling results across several tabs. Th ...
Chapter 1
... Data: facts and figures collected and summarized for a given topic, research or area of concern may be the most time consuming part of project (may also be very costly) obtained through internal systems (company databases), experiments, external sources (Hoovers), Gov’t agencies (Bureau of Labor ...
... Data: facts and figures collected and summarized for a given topic, research or area of concern may be the most time consuming part of project (may also be very costly) obtained through internal systems (company databases), experiments, external sources (Hoovers), Gov’t agencies (Bureau of Labor ...
Data warehouse and data mining
... Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Unsupervised learning (vs. supervised learning) Maximizing intra-class similarity & minimizing interclass similarity Outlier analysis Outlier: Data object that does not comply with the ...
... Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Unsupervised learning (vs. supervised learning) Maximizing intra-class similarity & minimizing interclass similarity Outlier analysis Outlier: Data object that does not comply with the ...
Machine Learning
... The basic PAC Model Distribution D over domain X Unknown target function f(x) Goal: find h(x) such that h(x) approx. f(x) Given H find heH that minimizes PrD[h(x) f(x)] ...
... The basic PAC Model Distribution D over domain X Unknown target function f(x) Goal: find h(x) such that h(x) approx. f(x) Given H find heH that minimizes PrD[h(x) f(x)] ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... the invention of tools and methods to segregate information that are hidden in such databases and hence the data mining concepts developed. The tradition definition of data mining [25] is “the non-trivial extraction of implicit, formerly unknown and practically beneficial information from data in da ...
... the invention of tools and methods to segregate information that are hidden in such databases and hence the data mining concepts developed. The tradition definition of data mining [25] is “the non-trivial extraction of implicit, formerly unknown and practically beneficial information from data in da ...
Data Mining on Student Database to Improve Future Performance
... Intelligence has boomed [1] [2]. This has led to an exponential growth in storage capacities hence leading to an increase in databases of several organizations. These databases contain important trends and patterns that can be utilized to improve success rate. Data Mining when applied to Educational ...
... Intelligence has boomed [1] [2]. This has led to an exponential growth in storage capacities hence leading to an increase in databases of several organizations. These databases contain important trends and patterns that can be utilized to improve success rate. Data Mining when applied to Educational ...
An Approach of Differential Geometry to Data Mining
... Data mining is defined as the process of extracting patterns and relationships, often previously unknown, from data sources that include data bases, collection data, or even data warehouse(Thuraisingham, 1997). Data mining is a step in a larger process of knowledge discovering in databases (KDD) tha ...
... Data mining is defined as the process of extracting patterns and relationships, often previously unknown, from data sources that include data bases, collection data, or even data warehouse(Thuraisingham, 1997). Data mining is a step in a larger process of knowledge discovering in databases (KDD) tha ...
View Sample PDF - IRMA International
... Data mining refers to a process on nontrivial extraction of implicit, previously unknown and potential useful information (such as knowledge rules, constraints, regularities) from data in databases. With the availability of inexpensive storage and the progress in data capture technology, many organi ...
... Data mining refers to a process on nontrivial extraction of implicit, previously unknown and potential useful information (such as knowledge rules, constraints, regularities) from data in databases. With the availability of inexpensive storage and the progress in data capture technology, many organi ...
Slide 1
... • Automated recommendation systems may be of interest at undergraduate level • But, in academia, beyond undergraduate it’s long tail all the way! • Small networks based on people actually knowing each other • Networks provide the opportunity for other people, known to me, to intervene on my behalf, ...
... • Automated recommendation systems may be of interest at undergraduate level • But, in academia, beyond undergraduate it’s long tail all the way! • Small networks based on people actually knowing each other • Networks provide the opportunity for other people, known to me, to intervene on my behalf, ...
the Knowledge Discovery Process in the SW
... E.g. 10-fold cross-validation is repeated 15 times and results are averaged → reduces the variance ...
... E.g. 10-fold cross-validation is repeated 15 times and results are averaged → reduces the variance ...
Educational Data mining for Prediction of Student Performance
... chosen sample 38 students record for our analysis. The confusion matrix demonstrates number of pass, fail and absentees for a particular examination. Number of pass students are 36. Number of Fail student is 1. Number of absentees is 1. The data analysis is performed with the methods of precision, r ...
... chosen sample 38 students record for our analysis. The confusion matrix demonstrates number of pass, fail and absentees for a particular examination. Number of pass students are 36. Number of Fail student is 1. Number of absentees is 1. The data analysis is performed with the methods of precision, r ...
d - UMK
... Meta-learning Meta-learning means different things for different people. Some will call “meta” learning of many models, ranking them, boosting, bagging, or creating an ensemble in many ways , so here meta optimization of parameters to integrate models. Landmarking: characterize many datasets and ...
... Meta-learning Meta-learning means different things for different people. Some will call “meta” learning of many models, ranking them, boosting, bagging, or creating an ensemble in many ways , so here meta optimization of parameters to integrate models. Landmarking: characterize many datasets and ...
A Review: Rare Event Detection In Weather forecasting Using Data
... the past. The centroid of the cluster is adaptive to the data that belongs to the cluster. This is the reason why we name it adaptive Markov chain model. An Adaptive Markov model: We proposed an adaptive Markov chain algorithm. Markov chains are an especially powerful and widely used tool for analyz ...
... the past. The centroid of the cluster is adaptive to the data that belongs to the cluster. This is the reason why we name it adaptive Markov chain model. An Adaptive Markov model: We proposed an adaptive Markov chain algorithm. Markov chains are an especially powerful and widely used tool for analyz ...
A Self-Adaptive Insert Strategy for Content-Based
... 2. Static versions are build up during a bulk load step. This enables the methods to create information about the data records so that they can use this information to optimize there structure. The disadvantage of this lies in its read-only usage. Thus, we were looking for a method to merge these tw ...
... 2. Static versions are build up during a bulk load step. This enables the methods to create information about the data records so that they can use this information to optimize there structure. The disadvantage of this lies in its read-only usage. Thus, we were looking for a method to merge these tw ...
Report on Evaluation of three classifiers on the Letter Image
... 1. The Decision tree and Random forest classifiers outpaced the famous Naïve Bayes classifier in terms of accuracy up to a large extent. This happened probably because of the assumption of feature independency we did in the case of Naïve Bayes, as some of the attributes here in our dataset were rel ...
... 1. The Decision tree and Random forest classifiers outpaced the famous Naïve Bayes classifier in terms of accuracy up to a large extent. This happened probably because of the assumption of feature independency we did in the case of Naïve Bayes, as some of the attributes here in our dataset were rel ...
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.