weka datasets

Tip: http://wanjiwz.blog.163.com/blog/static/2293491200911190199640/ When running the WEKA program, I found that all the text on the interface was displayed as small blocks. I searched the internet for the following solutions. I copied a Chinese font file to the JRE/lib/fonts/fallback directory, if there is no fallback directory, create one manually. My computer's JRE is under/usr/lib/JVM/java-1.6.0-openjdk-1.6.0.0/, but there is no fonts directo

The bagging part is relatively simple, and the algorithm and code are written together. I. Bagging Algorithm Strictly speaking, bagging is not a classification algorithm. Like boosting, bagging is a combination of basic classifiers, that is, using multiple base classifiers to obtain more powerful classifiers, its core idea is sampling with replacement. Training process of the Bagging Algorithm: 1. There are m samples from the sample set. 2. Use the m samples to train the base classifier C. 3.

Tags: source code algorithms, machine learning, WEKA Classifier Iii. Base Classifier The default base classifiers used by ipvstm1 are WEKA. classifiers. Trees. decisionstump. The name is a decision column (what is the name ?!), The classification method is similar to the node splitting Algorithm of ID3 algorithm. If it is Enumeration type, it traverses all attributes and selects one of them to maximize the

, its domain radius has a number of points Q, then for each q has q from the object P direct density can be reached. (3) algorithm flow Main flow: input e,minopt and object set n I, find an unmarked core object K, and set this object as marked. If the core object cannot be found, exit directly II. Extend this core object, expand (k) III, if all objects are marked, then exit, otherwise turn I Expand process: Input Core Object K I, initialize a set of S. Put K II, iterate over the collection eleme

Dbscan algorithm is a commonly used data mining algorithm. All clustering methods are divided into several types, the Kmeans algorithm discussed above is clustering based on partitioning, and the Dbscan algorithm mentioned in this paper is based on density. Of course, the other is based on hierarchical cohesion and division of methods, model-based approach, and so on. I first introduce and analyze the Dbscan algorithm implemented in Weka, and then ana

Tag:weka configuration database One, configuration file1, set Classpath,2. Use UTF-8 data set or file (can be omitted)To modify the Runweka.ini file under the installation directory, proceed as follows:Step 1: Open the Runweka.ini file with any text editorStep 2: Find fileencoding=cp1252 in line 32 and change Cp1252 to Utf-83. Configuring the Databaseutils.props FileWeka will only look for a configuration file named Databaseutils.props, if the user wants to use the rest of the configuration f

! IMG (img.ask.csdn.netupload201509281443409614_960824.png) description: 1. MySQL 5.5 dos windows are normal; 2. Java 1.7 dos windows are normal; 3. in weka3.7, MySQL is not configured normally before it is configured, and then the weka console is opened with problems. I 've been suffering for a day! Thank you for your guidance. mysqldosweka Note:1. MySQL 5.5 dos window is normal;2. the dos window of Java 1.7 is normal;3.

, multiply the weight of the original training set by W (that is, increase the weight ). (9) Normalize the weight of the training set (reduce the weight by a certain number and make it equal to 1) (10) return to 2 We can see that according to this algorithm, the base classifier is obtained after Step 4 training, and each base classifier gets a weight in Part 7, at last, weighted voting is performed based on the results of each base classifier to obtain the final result. II. Implementation When

Tags: style blog http OS AR for file data Download from http://www.cs.waikato.ac.nz/ml/weka for Windows, Mac, Linux Installation, changeable pathAnhao, as shown below RunOpen erxplore.Click Open file to open the data file.Click a data file in the installation directory, such as the data folder, to copy it to a common folder for easy use.Open a data file in the data folder, for example, weather. NominalThen, look at the various attri

Package Yuce;import Java.io.file;import weka.classifiers.classifier;import weka.classifiers.evaluation;import Weka.classifiers.trees.j48;import Weka.core.instance;import Weka.core.instances;import Weka.core.converters.ArffLoader; Public classtestclassification { Public Static voidMain (string[] args) {Try{File Inputfile=NewFile ("E:\\develop/weka-3-6/data/weather.numeric.arff"); Arffloader Loader=NewArffloader (); Loader.setfil

set is multiplied by W (that is, the weight is increased).(9) Normalized training centralization weight (reduced to a certain multiple and 1)(10) Back to 2Can see, according to this algorithm, the fourth step training to get the base classifier, each base classifier will get a weight in the seventh part. The final result is the weighted vote based on the results of each base classifier at the end of the classification prediction.Second, the realizationIn the analysis of each classifier, we all

which cluster center m_clusterassignments = initassignments (M_instances.numinstances ()); This two-dimensional array holds each cluster center with those instances, and it's very strange that weka are all using arrays. Instead of the list data structure. is expected to be considered in terms of efficiency. int[][] instofcent = new Int[m_clustercenters.numinstances () []; Inner iteration counter int kmeansiteration = 0; Hit log ignores

1. Configure Weka environment variables;2. Write Perl, as follows:########################################################################################################### ################### #!/usr/bin/perlUse strict;Use warnings;open FILE, ">./weka_pl_sys.txt" | | Die ($!);print FILE ' java weka.clusterers.simplekmeans-n 4-a ' weka.core.euclideandistance-r first-last "-I 500-num-slots 1 -S 200-t Example_cluster_id_h24_200.arff ';close FILE;######

0. Note the Chinese encoding of WekaRunweka.ini-----"Fileencoding=utf-81. First to the word-breaker after the discovery of the word breaker, converted to Arff file commandJava weka.core.converters.textdirectoryloader-dir D:\weibo\catagory\data10W\nlpirSegment\noNI > D:\weibo\ Catagory\data10w\nlpirsegment\weka\wb10w.arffFind transitions particularly fast2. Open the above file to generate the word vector, first select through features of the have, 1000

After studying the logical regression of the classification, continue to make a linear regression look. Linear regression in the field of data mining should also be very common, that is, based on the existing data set (matrix of row vectors), (training) to simulate a suitable law (function) to speculate on any new data combination (vector) should be the value of the. Specific description can see a variety of blogs, how to deduce it seems to see a little, but in summary The result is also simple,

(in selectmodel) (4) When splitting nodes for discrete values, the number of instances in the bag that exceed one is smaller than minnoobj (in spliter) (5) When the continuous value is split and calculated, the number of valid instances is less than 2 * minnoobj (in spliter) There are four stop conditions for reptree (1) The number of training sets is less than 2 * minnum (2) If the enumerated type is (3) If the value is of the numerical type, the variance is smaller than a given value. (4) rea

=== Run information === Scheme: WEKA. clusterers. simplekmeans-N 3-S 10Relation: aticleInstance: 372Attributes: 451[List of attributes omitted]Test Mode: Evaluate on training data === Model and Evaluation on training set === Kmeans====== Number of iterations: 11Within Cluster sum of squared errors: 6138.005013093585 Cluster centroids: Cluster 0Mean/mode: 0.0691 0.0619 0.0601 0.0571 0.0646 0.0468 0.0355 0.059 0.0573 0.055 0.0536 0.0278 0.033

The ARFF file format used in WEKA is divided into two parts: header and data. The header is used to define the relation name and a series of attribute names and types, such: @RELATION iris @ATTRIBUTE sepallength NUMERIC @ATTRIBUTE sepalwidth NUMERIC @ATTRIBUTE petallength NUMERIC @ATTRIBUTE petalwidth NUMERIC @ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica} Data, as its name implies, is data. The attribute sequ

> Java weka.clusterers.simplekmeans-p 1-l G:\Program\data_Factory\example.model-T G:\Program\data_Factory\ Save_file_id2class.arff 0 1 (0)1 2 (0)2 1 (0)3 3 ($)4 1 (0)> Java weka.clusterers.simplekmeans-l G:\Program\data_Factory\example.model-T G:\Program\data_Factory\save_ File_id2class.arffKmeans======Number of Iterations:8within cluster sum of squared errors:252.54315798169944Missing values globally replaced with Mean/modeCluster centroids:cluster#Attribute Full Data 0 1 2 3(+) (9) (139 ) (+)

Static voidSample (Instances Instances, Instances test)throwsexception{Random Rand=NewRandom (1023); Instances.randomize (RAND); Instances.stratify (10); Instances unlabeled= INSTANCES.TRAINCV (10, 0); Instances labeled= INSTANCES.TESTCV (10, 0); intiterations = unlabeled.numinstances ()/100 +1; for(inti=0; i){ //Select 5 instances with minimum entropy value per 100//100 a groupInstances resultinstances = Uncertaintysample (labeled, unlabeled, i*100, (i+1) *100); for(intj =