Datasets Over Algorithms
Datasets Over Algorithms. The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.
A meta-learning approach for recommending a subset of white-box classification algorithms for Moodle datasets
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This paper applies meta-learning to recommend the best subset of white-box classification algorithms when using educational datasets . A case study with 32 Moodle datasets was employed that considered not only traditional statistical features, but also complexity and
Bayesian comparison of machine learning algorithms on single and multiple datasets
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We propose a new method for comparing learning algorithms on multiple tasks which is based on a novel non-parametric test that we call the Poisson binomial test. The key aspect of this work is that we provide a formal definition for what is meant to have an algorithm that
Comparison of various classification algorithms on iris datasets using WEKA
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Classification is one of the most important task of data mining. Main task of data mining is data analysis. For study purpose various algorithm available for classification like decision tree, Navie Bayes, Back propagation, Neural Network, Artificial Neural, Multi-layer
Sampling algorithms for evolving datasets .
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Perhaps the most flexible synopsis of a database is a uniform random sample of the data; such samples are widely used to speed up the processing of analytic queries and data- mining tasks, to enhance query optimization, and to facilitate information integration. Most of
Performance comparative in classification algorithms using real datasets
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Classification is one of the most common data mining tasks, used frequently for data categorization and analysis in the industry and research. In real-world data mining sometimes it mainly deals with noisy information sources, because of data collection
New approach for classification of highly imbalanced datasets using evolutionary algorithms
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Abstract Todays most of the research interest is in the application of evolutionary algorithms . One of the examples is clas- sification rules in imbalanced domains. The problem of Imbalanced data sets plays a major challenge in data mining community. In imbalanced data
Comparison of classification algorithms using weka on various datasets
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Data mining is a step in the knowledge discovery process consisting of data mining algorithms that used to finds patterns or models in data. Data Mining also can be define as an analytic process designed to explore large amounts of data in search for consistent
Benchmarking algorithms for detecting anomalies in large datasets
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After reviewing state-of-the art anomaly detection algorithms and their effectiveness in dealing with both scattered and cluster anomalies, this research benchmarks the following algorithms based on their anomaly detection capabilities and their poly-logarithmic time
Comparative study of k-means, pam and rough k-means algorithms using cancer datasets
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Data mining is a search for relationship and patterns that exist in large database. Clustering is an important data mining technique. Because of the complexity and the high dimensionality of gene expression data, classification of a disease samples remains a
Review on density based clustering algorithms for very large datasets
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Data mining is widely employed in business management and engineering. The major objective of data mining is to discover helpful and accurate information among a vast quantity of data, providing a orientation basis for decision makers. Data clustering is
Scaling data mining algorithms to large and distributed datasets
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In the contemporary world of global economy real-life data is distributed and evolving consistently. For the purpose of data mining, the large set of evolving and distributed data can be handled efficiently by Parallel Data mining and Distributed Data Mining, Incremental
Detection of rare events within industrial datasets by means of data resampling and specific algorithms
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The paper deals with the problem of the detection of rare patterns in unbalanced datasets coming from the industrial world. Such kind of patterns usually correspond to not frequent but very relevant events, such as the occurrence of product defects and machine faults
Analysis of cancer datasets using Analysis of cancer datasets using Classification lassification lassification Algorithms Algorithms Algorithms
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Cancer detection is one of the important research topics in medical science. In bioinformatics age, gene expression data can be used for the cancer detection. Data mining techniques, such as pattern association, classification and clustering, are now frequently
Clustering algorithms for mixed datasets : A review
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Clustering is an essential technique in Data Mining which has been applied effectively in numerous perspectives. However, most of the clustering algorithms developed have been focused either on numeric or categorical datasets , but limited to both. Clustering algorithms
Analysis of Classification Algorithms J48 and SMO on Different Datasets
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Data mining is the forthcoming research area to solve different problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 and Sequential Minimal Optimization alias SMO of the Weka interface. It can
Performance evaluation of some online association rule mining algorithms for sorted unsorted datasets
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The association rules and its usage put forwarded lots of hopes in the field of data mining. The researchers in the field are going after the association rule mining techniques to find fastest as well as more precise association rules so that it will indirectly increase the profit of
Comparison of image reconstruction algorithms for the depiction of vessel anatomy in PC VIPR datasets
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Methods: Four renal PC VIPR data sets (two patients, two healthy volunteers) were acquired on a clinical 3T system (GE Healthcare) with a radially undersampled, dual echo sequence with balanced bipolar gradients and adaptive respiratory gating . Common imaging
Selecting classification algorithms with active testing on similar datasets
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Given the large amount of data mining algorithms , their combinations (eg ensembles) and possible parameter settings, finding the most adequate method to analyze a new dataset becomes an ever more challenging task. This is because in many cases testing all possibly
Classification of Complex UCI Datasets Using Machine Learning Algorithms Using Hadoop
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Classification is one of the most researched questions in machine learning and data mining. Classification is a gradual practice for allocating a given piece of input into any of the known category. The Data Mining refers to extracting or mining knowledge from huge volume of
Performance analysis of sequential pattern mining algorithms on large dense datasets
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ABSTRACT Sequential Pattern Mining involves applying data mining methods to large web data repositories to extract usage patterns. With the proliferation of Internet, discovery and analysis of useful information from the World Wide Web becomes a practical necessity. It has