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As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on o...

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication too...

"There are data mining methodologies for business intelligence (DM-BI) projects that highlight the importance of planning an ordered, documented, consistent and traceable requirement’s elicitation throughout the entire...

"We present research work in progress that focuses on data mining tools used for helping teachers to apply a three step knowledge discovering process to diagnose students’ misunderstandings (and their causes) related ...

More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These...

Materias / palabras clave: Statistical inference; Inference; Statistical learning; Computer science; Machine learning; Artificial intelligence; Data mining; Statistics; Mathematics

Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between it...

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression an...

In this paper we are concerned with the practical issues of working with data sets common to finance, statistics, and other related fields. pandas is a new library which aims to facilitate working with these data sets an...

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disci...

Materias / palabras clave: Cluster analysis; Computer science; Correlation clustering; Consensus clustering; CURE data clustering algorithm; Fuzzy clustering; Single-linkage clustering; Conceptual clustering; Artificial intelligence; Clustering hi...

Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussi...

"In sequential pattern mining, languages based on regular expressions (RE) were proposed to restrict frequent sequences to the ones that satisfy user-specified constraints. In these languages, REs are applied over items....

Since huge amounts of spatial data can be easily collected from various applications, ranging from remote sensing technology to geographical information system, the extraction and comprehension of spatial knowledge is a ...

SUMMARY: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis ta...

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information ...

A general inductive approach for analysis of qualitative evaluation data is described. The purposes for using an inductive approach are to (a) condense raw textual data into a brief, summary format; (b) establish clear l...

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a n...

BACKGROUND: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling...

Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to co...

SUMMARY: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequen...

categorical data analysis , categorical data analysis , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

OBJECTIVES: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this,...