stratum mining

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 interpretation of the original address Article 2: Learning from decision trees about Bayesian classifi

disadvantages of acquisition: 1. Do not respect other people's labor, imagine others hard for so long time to write the article, you a collection of software reprinted thousands of content, this is how bad, not to mention, few people will be in the collection of the original author's copyright. Seriously, you'll be in danger of being sued. 2. Do you have a station where anyone thinks of a dumpster? Although the content is important, consider the regularity of the content, the best selection of

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 data mining. For environment configuration problems, see: Http://blog.csdn.net/xinxing__8185/article/details/46445435

[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 purposes without explicit purpose. Therefore, data quality cannot be controlled at the data source. To avoid data quality problems, data mining focuses on two aspects: detection and co

(x)} = {p (x \bigcap y) \over p (x) p (y)} Close down properties (downward Closure property) If an item set satisfies a minimum support requirement, then any non-empty set of the set of items must satisfy this minimum support degree. Introduction to Apriori algorithm Apriori algorithm is a frequent itemsets algorithm for mining Association rules, whose core idea is to close and detect the frequent itemsets through the generation of candidate sets. Ap

Suppose you are the manager of a supermarket, you will want to understand the customer's shopping habits. You'll want to know what customers might buy at one time in the shopping, so you can arrange the shelves to make a bigger profit. This is the Association Rules (Association Rule). Its manifestations are as follows:bread⇒milk[support=10%;confidence=60%] Bread\rightarrow milk [support=10\%; confidence=60\%] The Support degree (support) and Confidence (confidence) of a rule are two metrics o

October 22 transferred to the Hcash (HSR) POS Mining Pool Mining proceeds to the account, there is a picture of the truth. October 22, I wrote a "teach you Hcash (braised pork) HSR How to Mine pool pos Mining Tutorial" At that time is also holding a small white mouse attitude, to try to see if there is a successful POS, the results turned 10.967 coins, as s

only 1. So the count of conditional pattern bases is determined by the minimum count of nodes in the path.Depending on the conditional pattern base, we can get the conditional FP tree for that commodity, for example i5:According to the conditions of the FP tree, we can do a full array of combinations, to get the frequent patterns excavated (here to the commodity itself, such as i5 also counted in, each commodity mining out of the frequent pattern mus

Data Mining Algorithm Learning notes SummaryData mining Algorithm (one) –k nearest neighbor algorithm (KNN)Data mining Algorithm (ii) – Decision treeData mining Algorithm (III.) –logistic regression Before introducing logistic regression, it is helpful to review a few basic knowledge points to help the understanding b

What is digging a mine. When we talk about Bitcoin, a noun that is often referred to is "digging", so what is it about digging a mine? What is the detailed process of it? Personally, I used to know that the so-called mining is about to find a random number, so that the hash value of the block is less than a certain number, but what is the specific. I don't know... And, of course, not only that, many other concepts like the structure of blocks, the s

[Introduction to Data Mining]-Introduction to data types and Data MiningData TypeDifferent datasets are manifested in many aspects. For example, attributes describing data objects can have different types: quantitative or qualitative. In addition, a dataset may also have a specific nature, such as a time series or associated with each other. This is because the data type determines what tools and technologies should be used for data analysis. In addit

1. Install NVIDIA graphics driver; then install Opencl/cuda http://blog.csdn.net/canhui_wang/article/details/72540004 2. Configure the local environment for Ethereum Mining sudo apt-gethttps://github.com/genoil/cpp-ethereum/blob/master/readme.md-y install Software-properties-common sudo add-apt-repository-y ppa:ethereum/ethereum sudo apt-get update sudo apt-get install git sudo apt-get install CMake sudo apt-get install Libcryptopp-dev sudo apt-

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

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 mining