Tags: blog HTTP Io use AR strong data SP Div I. Preface Every time we talk about data mining, some people come up with ETL, algorithms, and mathematical models. It is a headache for me to implement engineering. In fact, as for data mining, algorithms are only the means of
Titanic is a kaggle on the just for fun, no bonuses, but the data neat, practiced hand best to bring.Based on Titanic data, this paper uses a simple decision tree to introduce the process and procedure of processing data.Note that the purpose of this article is to help you get started with data mining, to be familiar w
observation data distribution characteristicSingle-Variable value grouping: Applies to discrete variables with less variable values.Group distance Grouping: Applies to continuous variables with more variable values.Ex: grouping methods and their watchmaking processesStep1: Determines the number of groups. The determination of group number is mainly used for the observation of data characteristics, so it de
First, the visualization method
Bar chart
Pie chart
Box-line Diagram (box chart)
Bubble chart
Histogram
Kernel density estimation (KDE) diagram
Line Surface Chart
Network Diagram
Scatter chart
Tree Chart
Violin chart
Square Chart
Three-dimensional diagram
Second, interactive tools
Ipython, Ipython Notebook
plotly
Iii. Python IDE Type
Pycharm, specifying a Java swing-based user interface
PyDev, SWT-based
All of the data mining code involved in this article is on my github:https://github.com/linyiqun/DataMiningAlgorithmIt took about 2 months to learn the classical algorithms of big data Mining and implement the code, which involved decision classification, clustering, link mining
heard that the complaint is: The model looks beautiful, but one to the application link to find that the prediction is inaccurate;2. Modeling means single, can not consider the problem in a multi-angle, so as to better fit the data;3. It is not possible to systematically compare the different models obtained by different methods, not to mention the selection of a relatively optimal model among many candidate models.At this point, to eliminate the abo
1. Data Mining classification: From the Perspective of data analysis, data mining can be divided into two types: Descriptive data mining-to express the existence of meaningful propertie
The previous series has talked about various kinds of knowledge, including drawing curves, scatter plots, power distributions and so on, and it becomes very important how to fit a straight line in a pile of scatter plots. This article mainly describes the Curve_fit function that calls the SCIPY extension package to achieve the curve fitting, simultaneously calculates the fitting function, the parameter and so on. Hope the article is helpful to you, if there are errors or deficiencies in the arti
Data Mining data analysis for online games Roadmap order:1) Build the basic data Warehouse;2) Wrong the user system:A) identification of the authenticity of user informationb) User grouping, segmenting the whole user into groups with specific attribute characteristics3) Organize da
Machine learning, data mining, and other
In this book, we constantly mention "intelligence". What is "intelligence "? Are we talking about artificial intelligence? Or machine learning? What does it have to do with Data Mining and soft computing? In academia, the exact defini
Data mining refers to the non-trivial process of automatically extracting useful information hidden in data from data collection, which is represented by rules, concepts, laws and patterns, etc.2.1 Development History of data mining
DataMining can be divided into three categories and six sub-items: Classification and Clustering belong to the Classification and segmentation class; Regression and Time-series belong to the prediction class; Association and Sequence belong to the Sequence rule class. Classification is calculated based on the values of some variables and then classified based on the results. (The calculation result is
Data Mining
This book provides a comprehensive overview of data mining, covering five topics: data, classification, correlation analysis, clustering, and anomaly detection. In addition to anomaly detection, each topic has two chapters. The previous chapter covers basic concepts, representative algorithms, and evaluation techniques
Data analysis and miningBaidu MTC is an industry-leading mobile application testing service platform, providing solutions for the costs, technologies, and efficiency problems faced by developers in mobile application testing. At the same time, we will share the industry's leading Baidu technology, written by Baidu employees and industry leaders.1. Overview 1.1 the key to the success of a mobile app is marketing and product design, the core of
enterprises.
With the rapid development of computer technology, network technology, communication technology, and Internet technology and the popularization of e-commerce, office automation, management information systems, and Internet, business operation processes of enterprises are increasingly automated, A large amount of data is generated during the enterprise's operation. These data and the resulting
0
S
T
S + T
Sum
Q + S
R + T
P = q + S + T + R
Now let's look at the similarity: Q and T. That is, similarity measurement: d (I, j) = (q + T)/P = (q + T)/(q + S + T + r)
Conversely, the opposite sex is a different measurement value .. That is, S and R, D (I, j) = (S + r)/P
Of course, what we calculate is symmetric binary. What is a symmetric Binary Attribute? Both are meaningful and important in reality.
Next, asymmetric binary similarity is assumed
hypothesis is obviously too strong,This is not necessarily the case. The use of the mean variance method also has similar problems. Therefore, the data normalization this step is not necessary to do, the specific problem to be seen. Normalization first in the case of a very large number of dimensions, you can prevent a certain dimension or some of the dimensions of the data impact too much, and then the pr
independent and has no correlation.If that is less than 0, the description is negatively correlated, and one value increases by another.Note that correlations do not imply causality, and if A and B are relevant, it does not mean that a causes B or B to cause a.3. Covariance of numeric dataCovariance and variance are two similar measures that evaluate how the two properties change together. The mean values of A and B are also known as expectations.The covariance of A and B is defined as: For
ObjectiveThis article continues our Microsoft Mining Series algorithm Summary, the previous articles have been related to the main algorithm to do a detailed introduction, I for the convenience of display, specially organized a directory outline: Big Data era: Easy to learn Microsoft Data Mining algorithm summary seria
1. Differences between statistics and data mining: Statistics mainly uses probability theory to establish mathematical models. It is one of the common mathematical tools used to study random phenomena. Data Mining analyzes a large amount of data, discovers internal links a
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