H. Witten/Eibe Frank's practical machine learning technology for data mining.
Collective intelligent programming is suitable for programmers who want to learn about data mining technology. This book describes many practical algorithms in data
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 minin
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
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
First, data mining
Data mining is an advanced process of using computer and information technology to obtain useful knowledge implied from a large and incomplete set of data. Web Data mining
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:
rule algorithm---AprioriFirst introduce a few professional nounsMining Datasets: The collection of data to be mined. That's a good understanding.Frequent patterns: Patterns that occur frequently in mining datasets, such as itemsets, sub-structures, sub-sequences, and so on. This is how to understand, in short, mining data
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 Se
transaction by user shell+ip+ hostname according to different user's login (all three are the same user) Based on this, the basic principle of mining 2 algorithm for user input command sequence frequent pattern is realized.
The fp-growth algorithm mainly solves the collection of frequent items where the number of occurrences reaches a certain threshold in multiple sets. A FP tree is a compressed representation of input
In today's big data era, data is money. With the transition to an application-based domain, data shows exponential growth. However, 80% of the data is unstructured, so it requires a program and method to extract useful information and convert it into an understandable and available structured form.
A large number
.
Pruning, satisfying the support and credibility of these 1-itemsets move to the next round of processes, and then look for the 2-itemsets that appear.
Repeat, the itemsets for each level are repeated, knowing the size of the itemsets we defined earlier.
is the algorithm supervised or unsupervised? Apriori is generally considered an unsupervised method of learning, as it is often used to excavate and discover interesting patterns and relationships.But, wait, there is ... The Aprior
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
and visualize data. Through various examples, the reader can learn the core algorithm of machine learning, and can apply it to some strategic tasks, such as classification, prediction, recommendation. In addition, they can be used to implement some of the more advanced features, such as summarization and simplification.I've seen a part of this book before, but the internship involves working with the data
First talk about the problem, do not know that everyone has such experience, anyway, I often met.Example 1, some websites send e-mails to me every few days, each e-mail content is something I do not interest at all, I am not very disturbed, to its abhorrence.Example 2, add a feature of a MSN robot, a few times a day suddenly pop out a window, recommend a bunch of things I don't want to know, annoying ah, I had to stop you.Every audience just want to see what he is interested in, rather than some
PrefaceRecently on the data mining learning process, learn to naive Bayesian operation Roc Curve. It is also the experimental subject of this section, the calculation principle of ROC curve and if statistic TP, FP, TN, FN, TPR, FPR, ROC area and so on. The ROC area is often used to assess the accuracy of the model, generally think the closer to 0.5, the lower the accuracy of the model, the best state is clo
process statistics, analyze and visualize data. Through various examples, the reader can learn the core algorithm of machine learning, and can apply it to some strategic tasks, such as classification, prediction, recommendation. In addition, they can be used to implement some of the more advanced features, such as summarization and simplification. I've seen a part of this book before, but the internship involves working with the
courses in the field of Java technology. Primarily Java-related technologies: Struts, Sping, Hibernate, Oracle, SQL Server, Hadoop, Memcache, Html, JavaScript, ActiveMQ.1. Deep mining of Big data2. Big Data storage3. Big Data Processing Solution4. Pure Distributed database: Cassandra5. The combination of cloud computing and database technology6. HDFS7, GANGLIA8.
are recorded by detailed XML files and displayed by RapidMiner graphical user interfaces. RapidMiner provides more than 500 operators for the main machine learning process, and combines a learning program with a property evaluator for the Weka learning environment. It is a standalone tool that can be used for data analysis as well as a data
Ipython is a python interactive shellAnaconda, packaged toolbox, type Eclipse becomes j2ee,android, can be installed on its own, or it can be the next ready versionSymPy Powerful Symbolic Data toolBased on the NumPy library, scipy function library adds many library functions which are commonly used in mathematics, science and engineering calculation. Examples include linear algebra, numerical solutions for ordinary differential equations, signal proce
With the advent of the big data age, the importance of data mining becomes apparent, and several simple data mining algorithms, as the lowest tier, are now being used to make a brief summary of the Microsoft Data Case Library.Appl
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