weka datasets

This code can be downloaded (updated tomorrow).In the previous article, the Hotspot Association rule Algorithm (1)-mining discrete data analyzes the hotspot Association rules of discrete data, and this paper analyzes the mining of the Hotspot Association rules of discrete and continuous data.1. First look at the data format (TXT document):@attribute Outlook {Sunny, overcast, rainy} @attribute temperature Numeric@attribute humidity Numeric@attribute Windy { TRUE, FALSE} @attribute play {yes, no}s

This code can be downloaded in http://download.csdn.net/detail/fansy1990/8502323.In the previous article, the Hotspot Association rule Algorithm (1)-mining discrete data analyzes the hotspot Association rules of discrete data, and this paper analyzes the mining of the Hotspot Association rules of discrete and continuous data.1. First look at the data format (TXT document):@attribute Outlook {Sunny, overcast, rainy} @attribute temperature Numeric@attribute humidity Numeric@attribute Windy { TRUE,

: This article mainly introduces 25 Java machine learning tools and libraries. For more information about PHP tutorials, see. 25 Java machine learning tools and libraries The IT industry is getting increasingly popular. with more new force joining the IT family, Java accounts for an increasing proportion. The following describes some learning tools. 1. Weka integrates machine learning algorithms for data mining. These algorithms can be directly applie

Weka1. Weka integrates a machine learning algorithm for data mining work. These algorithms can be applied directly to a dataset or you can write your own code to invoke it. Weka includes a range of tools, such as data preprocessing, classification, regression, clustering, association rules, and visualization. Massiveonlineanalysis2. Massiveonlineanalysis (MOA) is a popular open source framework for data str

Transferred from: http://www.cnblogs.com/data2value/p/5419864.htmlThis list summarizes 25 Java machine learning tools libraries:1. Weka integrates a machine learning algorithm for data mining work. These algorithms can be applied directly to a dataset or you can write your own code to invoke it. Weka includes a range of tools, such as data preprocessing, classification, regression, clustering, association

and 2D convolution layers. Linear Model and SVM Libraries Liblinear-a Library for Large Linear classification. It is also interfaced by Scikit-learn. Libsvm-state of Art SVM library with kernel support. It has also third-party plug-ins, if its built-in capabilities is not enough for you. Vowpal Wabbit-i Hear the name very often but haven ' t use it by now. However, it seems a decent library for fast machine learning. General Purpose Libraries Shougun-general Usage ML

series database on HBase;　　Prometheus: A time series database and service monitoring system; Newts: A time-series database based on Apache Cassandra.　　class SQL processing Actian SQL for Hadoop: high-Performance interactive SQL for access to all Hadoop data;　　Apache Drill: An interactive analysis framework inspired by Dremel;　　Apache Hcatalog:hadoop's table and storage management layer;　　Apache Hive:hadoop's class SQL Data Warehouse system;　　Apache Optiq: A framework that allows efficient query

Bloggers have recently started to explore Data Mining and share their study notes. Currently, WEKA is used. The next article will focus on this. Algorithm introduction: The K-means algorithm is a database with K input clustering numbers and N data objects. It outputs k clusters that meet the minimum variance standard. In addition, the obtained clustering satisfies the following requirements: the object similarity in the same cluster is high, while th

very important. It allows you to develop and expand new mining algorithms. In this regard, WEKA (idmer: Almost representative of open-source data mining software) provides a comprehensive documentation of Java functions and class libraries, which is very suitable for expansion. Of course, you must first fully understand the WEKA architecture and master Java programming technology. Another well-known open-s

is similar to processing, but nodebox is not interactive. Professional tools In addition to the several simple tools described above, there are also professional data processing tools for professionals to use. Industry-standard tools, such as SPSS and SAS, are expensive to order, so they are generally only available to large and academic institutions. The tools we will introduce are available for free and powerful. These open-source software are very useful and have powerful plug-ins and suppo

, such as SPSS and SAS, are expensive to order, so they are generally only available to large and academic institutions. The tools we will introduce are available for free and powerful. These open-source software are very useful and have powerful plug-ins and support. 18. R How many software comes with a search engine? R is a very complex software used to analyze the statistical data packages of large datasets. It has a powerful community and databa

strength is statistical analysis, which provides a wide range of parametric and parametric testing methods. At the same time, there are many feature selection methods. WEKA WEKA (Waikato environment for knowledge analysis, http://www.cs.waikato.ac.nz/ml/weka/) may be the most famous open source machine learning and data mining software. Advanced users can call

strategies you can take are: Compare some of the optional tools. Summarize the ability of the tool you have selected. Read and summarize the documentation for this tool. Complete the text or video tutorials for learning this tool, and summarize what you have learned in each tutorial. Make a tutorial on the features or features of this tool. Choose features you don't know well, write down the results, or take a five-minute screenshot of how to use the feature. Some

children's shoes that want to understand the algorithm directly to the classic paper; This book can be used as a supplementary reading for each of the two books. "Machine learning" (ml) PDF520Author Tom Mitchell is a master of CMU, with a machine learning and semi-supervised learning Network course video. This book is a good book for translation in the field, and the algorithms are much larger than the range of statistical learning methods. It is commented that the book is mainly about inspir

and visualize data. Through various examples, the reader can learn the core algorithm of machine learning, and can apply it to some strategic tasks, such as classification, prediction, recommendation. In addition, they can be used to implement some of the more advanced features, such as summarization and simplification.I've seen a part of this book before, but the internship involves working with the data in Java code, so let's put it aside for the moment and the book that is currently being Ha

classic paper; This book can be used as a supplementary reading for each of the two books. "Machine learning" (ml) PDFAuthor Tom Mitchell is a master of CMU, with a machine learning and semi-supervised learning Network course video. This book is a good book for translation in the field, and the algorithms are much larger than the range of statistical learning methods. It is commented that the book is mainly about inspiration, explain why the formula was founded rather than derivation; But som

receive the range. In the Weka Explorer page, there is a menu of select Attributes Selection propertiesAfter entering select attributesThere are attribute Evaluator (property evaluator), search method two options, both of which need to be combined with Property evaluatorsSubset EvaluatorsCfssubseteval evaluates the predictive capability of each attribute and its redundancy, preferring to choose attributes that are related to the category attribute, b

classic paper; This book can be used as a supplementary reading for each of the two books. "Machine learning" (ml) PDFAuthor Tom Mitchell is a master of CMU, with a machine learning and semi-supervised learning Network course video. This book is a good book for translation in the field, and the algorithms are much larger than the range of statistical learning methods. It is commented that the book is mainly about inspiration, explain why the formula was founded rather than derivation; But som

learning PR, which encapsulates many existing learning algorithms, such as WEKA, maxent, and SVM.Light, which converts the information of language features such as annotation attributes in lr to the input formats of various learning algorithms, and then calls the corresponding algorithms for output, then convert themIn addition, the algorithm effect can be evaluated. In gate machine learning, SVM-based Named Entity recognition is mainly based on [4.

, dive into one of the "live competitions currently running Onkaggle and give all-you has learnt a try!Step 8: Deep LearningNow so you had learnt most of the machine learning techniques, it was time to give deep learning a shot. There is a good chance that's already know what's deep learning, and if you still need a brief intro, here it's.I am myself new to deep learning, so please take the these suggestions with a pinch of salt. The most comprehensive resource is deeplearning.net. You'll find e