mahout hadoop

the case.City Block (or Manhattan) similarityThe taxi geometry or the Manhattan distance (Manhattan Distance) is a term created by the 19th century Minkowski, a geometrical term used in geometric metric spaces to mark the sum of the absolute wheelbase of two points on a standard coordinate system. The red line represents the Manhattan distance, the green represents the Euclidean distance, which is the straight line distance, while the blue and yellow represent equivalent Manhattan distances.The

Mahout in action in section 3.3 coping without preference values mentions data scenarios with no preference values. 1 ~ The five-point Preference Scoring scenario is too difficult, which requires a slightly complicated value judgment process. in contrast, "TOP" and "Step" are much simpler. as shown in the figure below, the boolean data model has been simplified to "like", "dislike", and "don't know ". The author provides a "sample code", which throw

Mahout is a commonly used similarity measurement based on Recommendation Systems, classification, and clustering algorithms: PearsoncorrelationsimilarityPearson distance EuclideandistancesimilarityEuclidean distance CosinemeasuresimilarityCosine distance (0.7ChangedUncenteredcosinesimilarity) SpearmancorrelationsimilarityThe Pearson distance after sorting. TanimotocoefficientsimilarityGrain correlation coefficient, based on Boolean prefer

The machine learning algorithms implemented in Mahout are shown in the following table Algorithm classes Algorithm name Chinese name Classification algorithm Logistic Regression Logistic regression Bayesian Bayesian Svm Support Vector Machine Perceptron Perceptron algorithm Neural Network Neural network Random fores

. [root@node1 ~]# cp mysql-connector-java-5.1.10-bin.jar sqoop-1.2.0-CDH3B4/lib[root@node1 ~]# cp hadoop-0.20.2-CDH3B4/hadoop-core-0.20.2-CDH3B4.jar sqoop-1.2.0-CDH3B4/lib[root@node1 ~]# chown -R hadoop:hadoop sqoop-1.2.0-CDH3B4[root@node1 ~]# mv sqoop-1.2.0-CDH3B4 /home/hadoop[root@node1 ~]# ll /home/hadoop total 3574

~]# cp Hadoop-0.20.2-cdh3b4/hadoop-core-0.20.2-cdh3b4.jar sqoop-1.2.0-cdh3b4/lib [root@node1 ~]# chown-r Hadoop:hadoop sqoop-1.2.0-cdh3b4 [root@node1 ~]# mv Sqoop-1.2.0-cdh3b4/home/hadoop root@node1 [~]# ll/home/hadoop Total 35748 -rw-rw-r--1 hadoop

weak mark, the T1 is added to the canopy (and this data can be used as a new canopy to calculate the distance of the other data to this point)(4) If this distance is less than T2, then the data is marked with a strong mark, and the data set is deleted, at this time that the data point is close enough to the canopy, it is impossible to form a new canopy(5) Repeat the 2-4 process until there is no data in the data setThe canopy here refers to the center of the data to be divided, with this canopy

1. Overview Mahout supports 2 kinds of m/r jobs to implement itembase collaborative filtering(1) Itemsimilarityjob(2) Recommenderjob SOURCE Package Location: Org.apache.mahout.cf.taste.hadoop.item.RecommenderJob Recommenderjob the first few stages and itemsimilarityjob are the same, but Itemsimilarityjob calculates the similarity matrix of the item is over, and Recommenderjob will continue to use the similarity matrix, For each user, the top N items

. out. println (St.Get("Jocularly")); }}This is the demo of the Detach Link:Package com.example.mahout; Public classListhashst_separate_chainingPrivate intM =8191;Privatenode[] St =NewNODE[M];Private Static classNode {Object key; Object Val; Node Next; Node (object key, Object Val, node next) { This. key = key; This. val = val; This. next = Next; } }Private int Hash(Key key) {return(Key.hashcode () 0x7fffffff)% M; } Public void put(Key key, Value Val) {inti = hash (key); for(Nod

method Stubtry {//Loading of data Building a data ModelDatamodel model = new Grouplensdatamodel (New File ("E:\\mahout Project \\examples\\ratings.dat")); Usersimilarity similarity = new org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity (model); Userneighborhood neighborhood = new Nearestnuserneighborhood (similarity, model);//Generate recommendation engine Recommender Recommender = New Genericuserbasedrecommender (model, neighb

oRg.apache.mahout.cf.taste.neighborhood.userneighborhood;import Org.apache.mahout.cf.taste.recommender.recommender;import org.apache.mahout.cf.taste.similarity.UserSimilarity; public class Genericbooleanpretest {public Genericbooleanpretest () throws Tasteexception, Ioexception{datamodel model = New Genericbooleanprefdatamodel (New Filedatamodel (New File ("E:\\mahout Project \\examples\\ua.base")); Recommenderirstatsevaluator evaluator = new Generic

most similar user group userneighborhood neighborhood = new feature (2, similarity, model); // The recommendation engine merges these components to recommend recommender = new genericuserbasedrecommender (model, neighborhood, similarity); // recommended item 1, 1, and list for user 1 When a new similarity measure is introduced, the results will change significantly. Mahout recommendation is composed of multiple components, rather than a single recom

weak mark, the T1 is added to the canopy (and this data can be used as a new canopy to calculate the distance of the other data to this point)(4) If this distance is less than T2, then the data is marked with a strong mark, and the data set is deleted, at this time that the data point is close enough to the canopy, it is impossible to form a new canopy(5) Repeat the 2-4 process until there is no data in the data setThe canopy here refers to the center of the data to be divided, with this canopy

Reprinted from (http://www.geek521.com/?p=1423)The Mahout recommendation algorithm is divided into the following major categoriesGenericuserbasedrecommenderAlgorithm:1. User-based similarity2. Similar user-defined and quantityCharacteristics:1. Easy to understand2. Fast calculation speed when the number of users is lowGenericitembasedrecommenderAlgorithm:1. Item-based similarityCharacteristics:1.item less time even faster2. It is very useful when the

Data mining has a lot of areas, classification is one of them, classification is to map some new data items to a given category in a category, such as when we publish an article, we can automatically divide this article into a certain article category, the general process is based on the sample data using a certain classification algorithm, The classification rules are obtained, and the new data is divided into categories according to the rules.Classification is a very important task in data min

1. Hadoop Java APIThe main programming language for Hadoop is Java, so the Java API is the most basic external programming interface.2. Hadoop streaming1. OverviewIt is a toolkit designed to facilitate the writing of MapReduce programs for non-Java users.Hadoop streaming is a programming tool provided by Hadoop that al

and commercial district. assume that the region where the mysql database is read is divided by region. I communicated with the leaders yesterday. The leaders said that the click-through rate is not a necessary condition, and the regional division is the focus, followed by persuasion from various aspects, so they had to distinguish the region. The key is that the town area distinguishes data from products, there are more than 6 K regions in China, The number of hdfs folders is not very bad,

Background Objectives: 1, Sport.tar is the sports category of articles, a total of 10 categories; The text classifier of sports class is constructed with these raw materials, and the effect of comparing Bayes and Cbayes is tested. Record the construction process and test results for the classifier. 2, User-sport.tar is a user browsing the article, each folder corresponds to a user; Using the text classifier constructed above, the paper calculates the proportion of each user browsing variou

transfer data from Hadoop and relational databases to and from a relational database to HDFs in Hadoop, or to the data in HDFs into a relational database. Mahout: Apache Mahout is a scalable machine learning and data Mining library that currently supports the main 4 use cases: Recommended mining: Collect user actions

high performance and stable functions. Zookeeper is an open-source implementation of Google's Chubby and a highly effective and reliable collaborative work system. Zookeeper can be used for leader election and configuration information maintenance. In a distributed environment, we need a Master instance or some configuration information to ensure file write consistency. Ubuntu 14.04 installs distributed storage Sheepdog + ZooKeeper CentOS 6 installs sheepdog VM distributed storage ZooKeeper clu