This document discusses Hadoop, HBase, Mahout, naive Bayes classification, and analyzing web content. It provides an example of using Mahout to train a naive Bayes classifier on web content stored in Hadoop and HBase. Evaluation results are presented, showing over 90% accuracy in classifying different types of web content. The effects of parameters like alpha values, n-grams, and feature selection are also explored.

1. Hadoop/Mahout/HBase
2011/04/10
#TokyoWebmining10-2
yanaoki
2011 4 18

2. •
• HBase
• Mahout
• Naive Bayes
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• Web
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3. •
• naoki yanai
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• …
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• Hadoop
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4. HBase
• KeyValue
• read/write
• goal is the hosting of very large tables -- billions of rows ,
millions of columns ...
• Hadoop
• CAP C,P
• C: ,A: ,P:
• Sharding
• Hadoop/MapReduce
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5. HBase
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• ―
• ―
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qualifier
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6. Mahout
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• Hadoop
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• HBase
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• Classifier / Clustering / Pattern Mining
• Recommenders / Collaborative Filtering
• Evolutionary Algorithms ...
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7. Mahout
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• Mahout
• Mahout in Action PDF
• hamadakoichi
• TokyoWebmining
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8. Naive Bayes
• F1,...,Fn C
• C
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9. Naive Bayes
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10. Naive Bayes
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11. • Web
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12. 2011 4 18

13. • Ruby
• ExtractContent
require "open-uri"
require "extractcontent"
html = open("http://
news.nifty.com/....htm").read
body, title = ExtractContent::analyse(html)
puts body.toutf8 #=> HTML
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14. • Ruby
• scrAPI
require 'scrapi'
require 'open-uri'
scr = Scraper.define do
process "div.tweet", "tweets[]"=> :text
result :tweets
end
tweets = scr.scrape(URI.parse("http://togetter.com/li/
121476"), :parser_options => {:char_encoding => 'utf8'})
tweets.each{ |tw| puts tw } #=>
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15. • RSS HBase
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(URL)
content categories
http://togetter/1.html category:src=”togetter”
...
category:cat=”social”
http:// category:src=”nifty”
news.nifty.com/....html AKB ...
category:cat=”entertainment”
http://groups.google.com/ 10
group/webmining-tokyo/
…
http://ameblo.jp/....html
KARA …
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16. • HBase
category_id <TAB>
• HBase MaprReduce HDFS
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• Wikipedia
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17. • mahout
$ mahout trainclassifier ...
$ mahout testclassifier …
• mahout
• --input/--output /
• --dataSource HDFS HBase
• --gramSize N-gram
• --classifierType
• --alpha
• --minDF/--minSupport /
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18. • HBase
•
=======================================================
Summary
-------------------------------------------------------
Correctly Classified Instances : 1884 82.2348%
Incorrectly Classified Instances : 407 17.7652%
Total Classified Instances : 2291
=======================================================
Confusion Matrix
-------------------------------------------------------
a b c d e <--Classified as
216 32 22 155 0 | 425 a = t
0 514 13 70 0 | 597 b = s
0 2 514 9 0 | 525 c = e
1 8 13 638 0 | 660 d = b
0 0 67 15 2 | 84 e = a
Default Category: unknown: 5
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19. •
• reducer HBase
//
BayesParameters params = new BayesParameters();
params.set("alpha_i", "1");
algorithm = new CBayesAlgorithm();
datastore = new HBaseBayesDatastore("model_table_name", params);
classifier = new ClassifierContext(algorithm, datastore);
//
ClassifierResult category = classifier.classifyDocument(doc.toArray(new String
[doc.size()]), "default");
String label = category.getLabel();
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20. •
(URL)
content categories
http://togetter/1.html category:src=”togetter”
...
category:cat=”social”
http:// category:src=”nifty”
news.nifty.com/....html AKB ...
category:cat=”entertainment”
http://groups.google.com/ 10
group/webmining-tokyo/ category:cat=”technology”
…
http://ameblo.jp/....html
KARA … category:cat=”entertainment”
2011 4 18

21. Web
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22. Web
• Google News Togetter
RSS
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• …
• …
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a 935 5.2M
b 5,112 7.2M
e 3,746 8.1M
s 4,737 12M
t 3,969 9.2M
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23. 4/18
Web
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=======================================================
Summary
-------------------------------------------------------
Correctly Classified Instances : 13388 91.6798%
Incorrectly Classified Instances : 1215 8.3202%
Total Classified Instances : 14603
=======================================================
Confusion Matrix
-------------------------------------------------------
a b c d e <--Classified as
2328 19 515 250 0 | 3112 a = t
3 2939 54 20 0 | 3016 b = e
32 3 3542 109 0 | 3686 c = s
33 16 128 3877 0 | 4054 d = b
1 27 2 3 702 | 735 e = a
Default Category: unknown: 5
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24. Web
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• alpha
1 0.5 0.1 0.01 0.001
65.38% 65.83% 66.73% 66.82% 67.02%
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25. 4/18
Web
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• N-Gram
unigram bigram
63.57% 66.09%
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26. Web
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+
56.8% 65.38%
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27. 4/18
Web
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67.02% 67.88%
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28. •
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• HBase/Mahout
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• HBase
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29. 2011 4 18