province to summarize sales data to view the Zhejiang-Shanghai area sales data. Slice (Slice): Select a specific value in the dimension for analysis, such as selecting only sales data for electronic products, or data for the second quarter of 2010. Cut (Dice): Select data f
products, or data for the second quarter of 2010.Cut (Dice): Select data for a specific interval in a dimension or a specific value for analysis, such as sales data for the first quarter of 2010 through the second quarter of 2010, or for electronic products and commodities.rotation (Pivot): That is, the position of the dimension of the interchange, like a two-di
[Introduction to Data Mining]-quality of data quality and quality of Introduction to Data MiningData qualityThe data used by data mining is usually collected or collected for other purp
Spatial data mining refers to the theory, methods and techniques of extracting the hidden knowledge and spatial relations which are not clearly displayed from the spatial database and discovering the useful characteristics and patterns. The process of spatial data mining and knowledge discovery can be divided into seve
Today introduces a book, "Data Mining R language combat." Data mining technology is the most critical technology in the era of big data, its application fields and prospects are immeasurable. R is a very good statistical analysis and dat
What is the use of data mining? What are the links between data mining and data warehousing? What are the links between data mining and market research, and
Read "Data Mining Technology (third edition)"-Thoughts on marketing, sales and customer relationship management
This book is not a purely data mining theory book, you can probably guess from the subtitle of this book. For a layman like me in the field of data
Tags: des http io ar os using for SP filesData mining Algorithm (analysis services–) Data mining algorithm are a set of heuristics and calculations that creates a data mining mOdel from data. "Xml:space=" preserve ">
I used to make some detours on Data Mining Research. In fact, from the origins of data mining, we can find that it is not a brand new science, but a combination of research achievements in statistical analysis, machine learning, artificial intelligence, and databases, in addition, unlike expert systems and knowledge ma
Business Intelligence product Data mining focuses on solving four types of problems: classification, clustering, correlation, prediction (which will be explained in detail after the four types of questions), while conventional data analysis focuses on solving other data analysis problems, such as descriptive statistics
Use excel for data mining (4) ---- highlight abnormal values and excel Data Mining
Use excel for data mining (4) ---- highlight Abnormal Values
After configuring the environment, you can use excel for
1. Define the mining target
To understand the real needs of users, to determine the target of data mining, and to achieve the desired results after the establishment of the model, by understanding the relevant industry field, familiar with the background knowledge. 2. Data acquisition and processing of clear
The previous article introduced the open source data mining software Weka to do Association rules mining, Weka convenient and practical, but can not handle large data sets, because the memory is not fit, give it more time is useless, so need to carry out distributed computing, Mahout is a based on Hadoop Cloth
With the intensification of market competition, China Telecom is facing more and more pressure, customer churn is also increasing. From the statistics, the number of fixed-line PHS this year has exceeded the number of accounts. In the face of such a grim market, the urgent task is to make every effort to reduce the loss of customers. Therefore, it is necessary to establish a set of models that can predict customer churn rate in time by using data
1 What is data mining?
The most commonly accepted definition of "Data Mining" is the discovery"Models" for Data.
1.1 statistical modeling
Statisticians were the first to use the term "data min
First contact data mining related knowledge, worship Daniel's article, hope to be able to add their own understanding
What is clustering, classification, regression.
Article 1: Data mining commonly used methods (classification, regression, clustering, association rules, etc.), slightly to the conceptual interpretatio
This paper is a partial translation and collation of the original English version of SQL Server data Mining Managed Plug-In algorithms tutorial, mainly describing the basic extension methods and development process of SSAS data mining algorithms. The content of this article
Spatial Data
Multimedia Data
For example, image data
Description-based retrieval system: keywords, titles, dimensions, etc.
Content-based retrieval system: color composition, texture, shape, object and wavelet transformation.
Time series data and sequence data
Trend Analysis
Purpose of collecting web logsWeb log mining refers to the use of data mining technology, the site user access to the Web server process generated by the log data analysis and processing, so as to discover the Web users access patterns and interests, such information on the site construction potentially useful and unde
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