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Because the next to do the seller background data vertical and horizontal revision, the data visualization of this piece of research and experience of the collation, with you to share the data visualization commonly used in five ways, hope to give you the development of idea
the contour factor of the clustering result, and is the reasonable and effective measure of the cluster.
1,FPC Bag
In the package FPC, some evaluation indexes of calculating clustering are realized, including contour factor: avg.silwidth (average contour width)
Library (FPC)
result
2,silhouette () function
The function silhouette () that calculates the contour coefficients in the package cluster returns the average contour width of the cluster:
Silhouette (x, dist, Dmatrix, ...)
Paramet
It happens that you are also a tool fan, and there is a little bit of research on data visualization and information visualization, where you can share some simple and fun visualization tools that you might be able to use.
When it comes to visualization, it's definitely a i
OData (Open Data Protocol) is always a standard I like (OASIS Standard), which provides a powerful access interface for querying and editing data based on restful protocols (Protocol). Although Microsoft launched it, it was born with open standards and open source genes (the first Microsoft opened the code for the OData client). When I have a chance, I'll go over some of the knowledge of OData in more detai
Seaborn Library Handbook Translation
Introductory Remarks:
Seaborn is actually a more advanced API encapsulation based on Matplotlib, making it easier to draw and, in most cases, using Seaborn to make attractive graphs. I am here to do my best to translate it (the dog has not seen the original computer in English before). ), convenient for everyone to inquire ~ ~ ~ Detailed Introduction can see Seaborn official API and Example Gallery one, style management 1, control picture art style
The abil
[Author]: KwuFast Echarts-based Big Data visualization, Echarts pure JS Implementation of the charting tools, the steps for rapid development are as follows:1, the introduction of Echarts-dependent JS Library2, set the display div3, the plot JSvar mychart;var option;//drawing function Drawcharts (Echartshomepath) {//Path configuration require.config ({paths: {echarts: Echartshomepath + ' JS '}})//use requir
"Tags": {"Host": "Mycat"7, for the JSON "QPS": AA of the QSP, equivalent to fields, for field8, for the statistical way, see the basic introduction of its INFLUXDB9. Statistics are often10, for the symbol of the curve, you can add multiple query in a diagram, so that each name corresponds to a different colorFinally click the Save button, at the top there is an icon, save after the selection has 4 blocks of the general's name just definedAt last:650) this.width=650; "src=" Http://s3.51cto.com/wy
A period of time completed a data visualization project, built by the background nodejs+highcharts framework. Let's share the process of the entire development process and the experience of using the Highcharts framework.First, the data readBecause the database is using MySQL database, in Nodejs, you can use the MySQL module in Nodejs for MySQL database operation
Webstorm+webpack+echartsEcharts Feature IntroductionEcharts, a pure Javascript chart library that runs smoothly on PCs and mobile devices, is compatible with most current browsers (Ie8/9/10/11,chrome,firefox,safari, etc.) and relies on lightweight Canvas class libraries Zrender provides intuitive, vivid, interactive and highly customizable data visualization charts.In Echarts 3, more rich interactivity and
First, the outsetFirst of all here to thank my company, because the company needs above the new (wonderful) needs, let me lucky to learn some fun and interesting front-end technology, front-end technology fun and more practical I think it should be a number of front-end data visualization this aspect, the current market data
Learn about graphical calculations for the components used to draw various permutations
The 1th part of this two-part series outlines the combined use of SVG and D3, providing some basic examples of creating browsing data visualization representations of social media. Part 2nd will describe the steps for arranging or laying out different graphic components in SVG graphics. You will learn how to use D3 powe
This two-part article series will demonstrate a visualization technique that helps extract information from the data that has business value, and this article is the first part of the series. You'll see how to use Scalable Vector Graphics (SVG) and open source D3 JavaScript libraries to create visual representations that can be viewed through the browser, conveying information through shapes and colors. I'l
OverviewMany scenarios in the business work need to visualize the data, in order to meet the needs of users, improve the user experience, we have developed more data visualization control. is introduced to everyone, forming a series.Today is the Chronicle of memorabilia control Verticaltimeline, which splits a series of events on a yearly basis, clicking on the y
segmentation situation. The red part is the process of running the program.11, continue to write code, the frequency of the statistical summary, the code implementation as shown.12, the program run, get a TXT and Excel file, inside is about the word frequency statistics information, as shown. The red part is the result of the program running, and there is no error.13. Import these keywords into WordArt for visualization, as shown in.14, set a case, f
for this project, they are all for the final visualization service. I strive to balance visual attractiveness with user interaction, allowing users to explore and understand the subject's trends without guidance. The diagram I started with was stacked blocks, and I realized that simple line drawings were sufficient and clear.
I use D3.js for visualization, which is appropriate for the
, where you can design the main body of the new analysis.650) this.width=650; "src=" http://pic.pc6.com/up/2015-1/14224140158935530.jpg "style=" border:0px; "alt=" 14224140158935530.jpg "/>Here, the data source and the new data analysis are done, and the next step is to do a detailed analysis of the data, such as documentation examples.Gender dimension analysis b
= Rev (levels (Uspopage$agegroup)))Operation Result:If you need to draw a percentage stacked chart, simply modify the underlying data based on the work above.The R language implementation code is as follows:# Convert data to percent format Uspopage_prop = ddply (Uspopage, "year", transform, Percent = Thousands/sum (Thousands) * 100) # base function Ggplot (USP Opage_prop, AES (x = year, y = Percent, fill =
method is simple to add the marginal carpet function on the basis of the original scatter plot function. The R language implementation code is as follows:# base function Ggplot (Faithful, AES (x = eruptions, y = waiting)) + # Scatter graph function geom_point () + # Marginal carpet function Geom_rug () Operation Result:add a label to a scatter plotThis example uses the following test data set:The method of adding labels to scatter plots is als
! Hoststate::up hoststatetype::hard Servicestate::ok servicestatetype::hard graphiteprefix::jenkins GRAPHITEPO Stfix::swap metrictype::$_servicemetrictype$When you see the above configuration file, there is a question: Why do you want to change the file to this format? The reason is that Nagios's data wants to be transferred to INFLUXDB and needs graphios to act as a porter, Graphios's code is written in Python, where a piece of code is designed to t
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