tools used for data mining

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Use WEKA for data mining-Chapter 2: Regression

house has been inserted.Listing 3. housing prices using regression models sellingPrice = (-26.6882 * 3198) + (7.0551 * 9669) + (43166.0767 * 5) + (42292.0901 * 1) - 21661.1208sellingPrice = 219,328 However, looking back at the beginning of this article, we know that data mining is not just about outputting a value: it is about recognition patt

Data mining with Weka, part 3rd nearest neighbor and server-side library

Brief introduction In the two articles before the "Data mining with WEKA" series, I introduced the concept of data mining. If you haven't read data mining with Weka, part 1th: Introduction and regression and

What is data mining?

particular way or used in some commonly used representations.Knowledge assessment will present the knowledge found in a way that the user can understand, optimizing certain processing stages in the knowledge discovery process as needed until the requirements are met. Thus, data mining is only one step of knowledge di

Data mining with Weka, part 2nd classification and clustering

Brief introduction In data mining with WEKA, part 1th: Introduction and regression, I introduced the concept of data mining and free open source software Waikato Environment for Knowledge Analysis (WEKA), which can be used to mine data

Based on. NET realizes data mining--time Series Algorithm 1

Http://www.cnblogs.com/captain_ccc/articles/4093652.html This article is also the continuation of the Microsoft Series Mining algorithm Summary, the previous several mainly based on state discrete value or continuous value for speculation and prediction, the main algorithm used is three: Microsoft Decision tree Analysis algorithm, Microsoft Clustering Analysis algorithm, Microsoft Naive Bayes algorithm ,

R Language Common Data mining package

data mining, so they are included.1. Clustering Commonly used packages: Fpc,cluster,pvclust,mclust Partitioning-based approach: Kmeans, Pam, PAMK, Clara Hierarchy-based approach: Hclust, Pvclust, Agnes, Diana Model-based approach: Mclust Density-based approach: Dbscan Drawing-based method: Plotcluster, Plot.hclust Verification-ba

python& Data analysis & Data Mining--reference books

the required package again.4, after learning the introductory book, you need to learn how to use Python to do data analysis, recommend a book: using Python for data analysis, this book mainly introduces the data analysis of several commonly used modules: NumPy, pandas, Matplotlib, and

Application of learning hash and hash in big data retrieval and mining

valid tive methods for big data retrieval and mining. Due to the low storage cost and high query speed of hash, it is widely used in the approximate Nearest Neighbor Search of big data. The basic idea of hash is to map the data points in the original feature space into the

"Python Data Mining Course" seven. PCA reduced-dimension operation and subplot plot __python

This article mainly introduces four knowledge points, which is also the content of my lecture. 1.PCA Dimension reduction operation; PCA expansion pack of Sklearn in 2.Python; 3.Matplotlib subplot function to draw a child graph; 4. Through the Kmeans to the diabetes dataset clustering, and draw a child map. Previous recommendation:The Python data Mining course. Introduction to installing Python and crawler"

Data Mining Video Tutorial Download

Principles of data mining and actual combat: Link: http://pan.baidu.com/s/1qWFNuPm Password: oa4nPlease add qq:3113533060 if the net disk is invalid.1th Week Data Analysis basicsKey points data analysis process, methodology (PEST, 5W2H, logical tree), basic data analysis met

Python VS R language? Data analysis and mining which one should I choose?

packages are written by the R language, LaTeX, Java, and the most commonly used C language and Fortran. The version of the executable that you download will be accompanied by a batch of core features, and there are thousands of different packages based on the Cran record. Several of them are more commonly used, such as economic metrology, financial analysis, humanities research, and artificial intelligence

Summary of ten algorithms of data mining--core idea, algorithm advantages and disadvantages, application field

The algorithm in this paper only outlines the core idea, the specific implementation details of this blog "Data Mining Algorithm learning" classification under other articles, not regularly updated. Reprint please indicate the source, thank you.Referring to a lot of information and personal understanding, the ten algorithms are categorized as follows:? Classification algorithm: C4.5,cart,adaboost,naivebayes

Hadoop mahout Data Mining Practice (algorithm analysis, Project combat, Chinese word segmentation technology)

Foundation, learn the North wind course "Greenplum Distributed database development Introduction to Mastery", " Comprehensive in-depth greenplum Hadoop Big Data analysis platform, "Hadoop2.0, yarn in layman", "MapReduce, HBase Advanced Ascension", "MapReduce, HBase Advanced Promotion" for the best.Course OutlineMahout Data Mining

Data Mining-Understanding data

]} = \frac{|x_{if}-x_{jf}|} {\max_{h} x_{hf}-\min_{h} X_{HF} $, where h passes all non-missing objects of property F. F is nominal or two yuan: if \ (x_{if} = x{jf}\), then \ (d_{ij}^{[f]}=0\), otherwise take 1. F is ordinal: computes the rank \ (r_{if}\) and \ (z_{if} = \frac{r_{if}-1}{m_f-1}\)and then processes it as a numeric attribute. Cosine similarityTo compare documents, each document is represented by a so-called word frequency vector, usually very long and sparse, and the t

Data mining-understanding data

1. a dataset consists of data objects. A Data Object (sample, instance, data point, object, and data tuples) represents an object. Ii. Attribute types An attribute is a data field that represents a feature of a data object. The a

What is data mining? What's the use?

personalization also need data mining technology support, such as Taobao, according to the user's search habits, the introduction of users like products. Mining objects In principle, data mining can be carried out on any type of dat

Data mining process: Data preprocessing

is, each index value is at the same quantity level, can carry on the comprehensive evaluation analysis.the normalization process of data is also a normalization process. The standardization of data (normalization) is to scale the data proportionally to a small, specific interval. In some comparison and evaluation of the indicator processing is often

New Internet: Big Data Mining ebook PDF download production customization Service

Content recommendationNew Internet: Big Data Mining provides a comprehensive overview of how data mining technology can be used to extract and generate business knowledge from a wide variety of structures (databases) or unstructured (WEB) mass

Data Mining Overview

Recently, I have the opportunity to access some data mining things.I personally feel that this technology will certainly have a great development prospect.So I will use this article to explain my views on data mining.The concept of data mining is explained step by step. (1)

Microsoft Data Mining Development: validation and presentation of the model

Validating a data mining model Typically, for a particular case, we can't pinpoint which mining algorithm is the most accurate, so we define multiple mining models in a mining structure, and we get the most accurate one by validating multiple

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