association rules python

Target :Using pathological information to explore the relationship between TCM syndromes and TNM staging of breast cancer thinking and Flow :Objective to explore the relationship between TCM syndromes and TNM staging of breast cancer, and adopt Association rules modelAfter confirming the model, the data of the patients ' TCM syndromes and the TNM staging of breast cancer need to be sorted out. The data shou

of a certain attribute of a new data based on the massive historical data, combined with certain algorithms, and based on probability. Many data mining models, such as Bayesian, time series, and association rules, are common models. Different model algorithms can be applied based on different problem features. For example, the product recommendation mentioned in this article is typically suitable for solvi

Several basic concepts and two basic algorithms for association rules are described in the previous few. But actually in the commercial application, the writing algorithm is less than, understands the data, grasps the data, uses the tool to be important, the preceding basic article is to the algorithm understanding, this article will introduce the open source utilizes the data Mining tool Weka to carry on t

Association rules are expressions such as a->b, and A and B are the two subkeys that intersect each other in the entire set.The main purpose of mining association rules is to find meaningful correlation relationships in data. Shopping basket analysis is the analysis of customer purchase behavior to discover the relatio

--apriori Algorithm for association rules the concept of associative patterns in some of the discussions emphasizes the simultaneous occurrence of relationships while ignoring the sequence information in the data (time / space):time series : Customers buy product X, it is possible to buy products in a period of time Y;Spatial Sequence : A is found at a point , and it is possible to find the phenomenon Yat t

: bread diaper 3 diaper milk 3 milk 4 bread milk 3 Diaper beer 3 Bread 4In addition I downloaded a shopping basket data set, The volume of data is large, testing the efficiency of the fpgrowth is Good. The average efficiency of the fpgrowth algorithm is much higher than the Apriori algorithm, but it does not guarantee high efficiency, its efficiency depends on the data set, when there are no common items in the frequent itemsets in the dataset, all itemsets are hung

example, the first line points to I2:7, and the second line points to I1:4 because the I1 node also appears elsewhere in the FP tree, and a pointer to the I1:2 node is stored in the so-called I1:4 nodeBuilding a good FP tree with a handful of full-table scans turns the cart's irregular data into a tree-like structure that can be traced, and eliminates the computational nature of a huge natural connection.Mining Association

First of all, the source code analysis of the mahout association rules in the previous article is incorrect in many parts of part2. Now I will re-write the following: run the following command on the command line to obtain the usage of the association rule FPGrowthDriver for mahout: [java] bin/hadoop jar $ mahout_home/core/target/mahout-core-0.7-job.jar org. apac

In data miningAlgorithmIt is a widely used association rule mining algorithm. The single-minimum-support algorithm and multi-minimum-support algorithm can be considered as a special case of the Multi-minimum-support algorithm. In practical applications, the use frequency of the Multi-minimum support algorithm is relatively high. In many books about data mining, we have provided detailed false data of the MS-Apriori algorithm.CodeIn this algorithm, we

the item list to be passed and call the service by calling the getmodelresultasync method. Note that the parameter received in the previous service definition is list, which is automatically converted to arrarofstring on the client side. After adding the above code, you can test our prediction query through the interface in the previous section. We can see that different products are recommended to us by selecting different products based on the mining model. So far, this simple front-end a

than the Apriori algorithm, but it does not guarantee high efficiency, its efficiency depends on the data set, when there are no common items in the frequent itemsets in the dataset, all itemsets are hung on the root node, the compressed storage is not implemented, and the Fptree requires additional overhead. Need more storage space, before using the fpgrowth algorithm, analyze the data to see if it is appropriate to use the FPGROWTH algorithm.Reference Document: Http://www.cnblogs.com/fengfeng

bp9g39,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Lanru b7m827, 1,23,0,1,0,0,2,55,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Lanru Bq3m79, 0,11,0,0,0,0,1,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Lanru bu008p, 0,4,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Lanru BW6710, 14,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0 Lanru bs180g, 0,1,0,0,0,0,0,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Lanru b3hu73, 1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0

Details about the scope rules in Python and the python domain rules Python is a static scope language, although it is a dynamic language. That is to say, in Python, the scope of a variable is determined by its position in the sour

Scope rules and closures of Python functions, and python function domain rulesScope rules The namespace is a ing from name to object. In Python, it is mainly implemented through dictionaries, mainly including the following namespaces: The built-in namespace contains some b

Talking about the encoding rules in Python and the python encoding rules Note: I use Python3.4 as the learning version. The following learning experiences are only applicable to Python3.4. I read the answer to the code from Alex kingdom, and I have gained a lot. I would like to thank you for your understanding of Encod

you can define a minimum degree of support and keep only the set of items that meet the minimum support. Confidence or confidence level (confidence) The confidence level is defined for an association rule such as {diaper}→{wine}. The credibility of this rule is defined as "support ({diaper, wine})/support ({diaper})". In the example above, because the support degree of {diaper, wine} is 3/5,{diaper} is 4/5, the trustworthiness of "diaper → wine" is 3

"Python Concurrency Association"One: Concurrency is implemented on a single thread, that is, only one main thread, and only one CPU is implemented concurrently. (Nature of Concurrency: Toggle + save State) The CPU is running a task, and in both cases it will be cut to perform other tasks (the switchover is controlled by the operating system), in which case the task is blocked and the second case is that t

production: A file path that contains the Python line shop grep begins production Product: A file path containing the Python line the shop grep begins to produce a product: A file path that contains a python line of content shop grep begins to produce a product: A file path that contains the Python line shop grep begi

of multi-core, and give full play to the high efficiency of the association, get very high performance,Python multi-process support is implemented through generator.In generator, not only can we iterate through a for loop, but we can constantly call next () to get the next value returned by the yield statementBut the yield of Python can not only return a value,

; If the context has a section of code can be divided into relatively independent two parts, if the independent two parts are CPU-intensive, then use multi-process, if it is IO-intensive, then use the coprocessor, if both are involved, you can consider using a child process to run the coprocessor. Business code to quickly create a thread or process, while enhancing the readability of the code, it is recommended to use anonymous functions. from redis import StrictRedisrs = StrictRedi