Data warehouses usually have some missing values due to unavailable data that affect the
number and the quality of the generated rules. The missing values could affect the coverage
percentage and number of reduces generated from a specific data set. Missing values lead to the
difficulty of extracting useful information from data set. Association rule algorithms typically
only identify patterns that occur in the original form throughout the database.
This paper proposes a novel approach for effectively utilizing unsupervised data in addition to supervised data for supervised learning. We use unsupervised data to generate informative ‘condensed feature representations’ from the original feature set used in supervised NLP systems. The main contribution of our method is that it can offer dense and low-dimensional feature spaces for NLP tasks while maintaining the state-ofthe-art performance provided by the recently developed high-performance semi-supervised learning technique. ...
Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work....
The book is written as a reference guide. It includes fully working examples based on a real-world public data set. This book is for developers who want to learn how to use Apache Solr in their applications. Only basic programming skills are needed.
Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups.
Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations
Reduce the size of large data sets
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A large, consistent plasma proteomics data set from prospectively collected breast cancer patient and healthy volunteer samples
How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing"....
Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài : A large, consistent plasma proteomics data set from prospectively collected breast cancer patient and healthy volunteer samples
Methods for analysing trauma injury
data with missing values, collected at a UK
hospital, are reported. One measure of injury
severity, the Glasgow coma score, which is
known to be associated with patient death, is
missing for 12% of patients in the dataset. In
order to include these 12% of patients in the
analysis, three different data imputation
techniques are used to estimate the missing
Overview of data mining. Emphasis is placed on basic data mining concepts. Techniques for uncovering interesting data patterns hidden in large data sets. Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years.
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Accuracy of phylogeny reconstruction methods combining overlapping gene data sets...
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of diﬀerentially expressed genes (Open Access publication)
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Identification of tissue-specific, abiotic stressresponsive gene expression patterns in wine grape (Vitis vinifera L.) based on curation and mining of large-scale EST data sets
Transforming syntactic representations in order to improve parsing accuracy has been exploited successfully in statistical parsing systems using constituency-based representations. In this paper, we show that similar transformations can give substantial improvements also in data-driven dependency parsing. Experiments on the Prague Dependency Treebank show that systematic transformations of coordinate structures and verb groups result in a 10% error reduction for a deterministic data-driven dependency parser.
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Radiation Oncology cung cấp cho các bạn kiến thức về ngành y đề tài: " Investigation of the usability of conebeam CT data sets for dose calculation...
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo...
In this paper, we systematically assess the value of using web-scale N-gram data in state-of-the-art supervised NLP classiﬁers. We compare classiﬁers that include or exclude features for the counts of various N-grams, where the counts are obtained from a web-scale auxiliary corpus. We show that including N-gram count features can advance the state-of-the-art accuracy on standard data sets for adjective ordering, spelling correction, noun compound bracketing, and verb part-of-speech disambiguation. ...