filecoin mining

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 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 i

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 mining ." Now, statisticians view data mining as the construction ofStatistical Model, That is,Underlying Distribution(EX. Gaussian

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 mining, there is not much difficulty in reading this book. This book is not a pure technology book, but

Frequent pattern mining can be a lot of patterns, but judging whether a pattern is interesting requires a pattern evaluation method. The common pattern evaluation methods are described below. (Hypothetical set of items A, B) 1. Support Degree The ratio of the number of tuples in the item set A and B to the number of all tuples, typically P (a∪b). 2. Reliability The confidence level of mode a--> B is P (b| A 3. Lifting Degree Lift (A, B) = P (a∪b

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 data mining software, R language features is easy to get started, easy to use.This book focuses on the use of R for data

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 understandable unknown information and knowledge, for the analysis of the site's access to the

I plan to organize the basic concepts and algorithms of data mining, including association rules Mining, classification, clustering of common algorithms, please look forward to. Today we are talking about the most basic knowledge of association rule mining. Association rules mining has been widely used in electric bus

March 10, there are netizens in the group to share the suspected Xiaomi blockchain products "encrypted rabbit" link, Xiaomi or will launch their own blockchain game project.First, about the millet blockchain pet "Crypto Rabbit"From the exposure of the 2 group chat, Xiaomi should formally enter the Blockchain game field.From millet "encrypted rabbit" to see the blockchain game, Mining has become a marketing toolWelcome to Encrypt Rabbit Blockchain Pet

In various data mining algorithms, association rule mining is an important one, especially influenced by basket analysis. association rules are applied to many real businesses, this article makes a small Summary of association rule mining. First, like clustering algorithms, association rule mining is an unsupervised le

Tags: using SP data, BS, users, technical objects, different methods First: Data type, Different attributes of an object are described by different data types, such as age --> int; birthday --> date. Different types of data mining must be treated differently. Second: Data quality, Data quality directly affects the quality of the mining results. Generally, noise, outlier, data omission, and duplication in da

I. Concepts Association Rule Mining: discovering interesting and frequent patterns, associations, and correlations between item sets of a large amount of data, such as the food database and relational database. Measurement of the degree of interest of association rules:Support,Confidence K-item set: a set of K items Frequency of the item set: number of transactions that contain the item set Frequent Item Set: if the frequency of the item set is greate

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, cross-reporting, hypothesis testing, etc. Data mining is a very clear definition of the

The Application of association rule Mining algorithm in life is everywhere, it can be seen in almost every e-commerce website.To give a simple examplesuch as Dangdang, when you browse a book, you can see some package recommendations on the page, book + related books 1+ related books 2+...+ Other items = How many ￥And these packages are likely to suit your appetite, and you might have bought a whole package for this recommendation.This is different fro

Python data analysis, R language and Data Mining | learning materials sharing 05, python Data Mining Python Data Analysis Why python for data analysis? In terms of data analysis and interaction, exploratory computing, and data visualization, Python will inevitably be close to other open-source and commercial programming languages/tools, such as R, MATLAB, SAS, and Stata. In recent years, Python has continuo

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 management, Data Mining focuses more on the Ap

1 Algorithm Design Objectives Entering different commands is the basic way for users to use the Linux server, through a long time to collect different users in the use of the server process of the command sequence, mining the frequent occurrence of the command sequence, can help us understand the user to use the basic rules of the server. In addition, if there are more than one server, then we can analyze mining