Apache Mahout

More documents

Recommendations

Info

r M A P | SHUFFLE | R E D U C E output input Local to data. Outputs a lot less data. Output can cheaply move. Shuffle sorts input by key. Reduces output significantly.
private IntWritable one = new IntWritable(1); private Text hostname = new Text(); public void map(LongWritable key, Text value, OutputCollector output, Reporter reporter) throws IOException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { hostname.set(getHostname(tokenizer.nextToken())); output.collect(hostname, one); } } public void reduce(Text key, Iterator values, OutputCollector output, Reporter reporter) throws IOException { int sum = 0; while (values.hasNext()) { sum += values.next().get(); } output.collect(key, new IntWritable(sum)); }
Page 1 and 2: Apache Mahout Large Scale Machine L
Page 3 and 4: Hello DAI!
Page 5 and 6: January 3, 2006 by Matt Callow http
Page 7 and 8: September 21, 2008, Rodrigo Galinde
Page 9 and 10: Machine learning - what's that?
Page 11 and 12: Archimedes model of nature
Page 14 and 15: An SVM's model of nature
Page 16 and 17: ● Industry ready. - Friendly lice
Page 18 and 19: January 8, 2008 by Pink Sherbet Pho
Page 20 and 21: Developers world wide
Page 22 and 23: Developers world wide Developers in
Page 24 and 25: Developers world wide Developers in
Page 26 and 27: Typical developer September 10, 200
Page 28 and 29: Requirements ● Easy administratio
Page 30: Java based implementation. Easy dis
Page 33 and 34: Contributions need not be Hadoop ba
Page 35 and 36: pattern=”http://[0-9A-Za-z\-_\.]*
Page 37 and 38: Assumptions: Data to process does n
Page 39 and 40: M A P | SHUFFLE | R E D U C E Local
Page 41: M A P | SHUFFLE | R E D U C E outpu
Page 45 and 46: Options for running Hadoop ● Amaz
Page 47 and 48: Step 1: Serial implementation.
Page 49 and 50: Step 2: Parallelization strategies.
Page 51 and 52: Step 2: Parallelization strategies.
Page 53 and 54: Algorithm properties ● Job local
Page 55 and 56: Discover groups of items ● Group
Page 57: Going parallel: k-Means
Page 63 and 64: Until stable. Data intensive. Outpu
Page 65 and 66: Assign items to defined categories.
Page 67 and 68: Evolutionary algorithms ● Traveli
Page 69 and 70: Recommendation mining. ● ● ●
Page 71 and 72: What next? ● More algorithms. ●
Page 73 and 74: Reasons: Promotion, recruiting, and
Page 75 and 76: June 13, 2008, Star Guitar http://w
Page 77 and 78: Ted Dunning: “[...] if I have a c
Page 79 and 80: Jumpstart your project with proven
Page 81 and 82: Become part of the community.
Page 83 and 84: Sept., 29 th 2009: Hadoop* Get Toge
Page 87 and 88: Open questions. Gather data Large s
Page 89 and 90: Algor. choice Gather data Extract f
Page 91 and 92: Algor. choice Parameters Train mode
Page 93:
Algor. choice Parameters Train mode
show all

Apache Mahout

Create successful ePaper yourself

Delete template?

Save as template?