FpGrowth

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Java implementation of the Frequent Pattern Growth ( FP-Growth ) algorithm, which is a scalable method for finding frequent patterns within large datasets. For example, it could be used to find Association Rules and develop collaborative-filtering  systems, such as "Other people also bought"...  The algorithm takes three arguments: Dataset : path to a local (data://...) dataset, where each line represents a single transaction and each item is separated by whitespace. (See Example file). Support : represents the minimum frequency for a pattern to be recognized. In most cases you'll want to increase this number to reduce the size of the output. MinItems : represents the minimum number of items (per association rule). Having the value of this argument as 1 will return each unique item in the dataset and the number of times they appeared. Most applications would require a number higher than 1. Output : optionally specify a local (data://...) location to which the output JSON should be written. This is required for result sets exceeding 10Mb in size. This algorithm was featured in the Algorithmia Blog Post: "Mining Product Hunt, Part 2: Building a Recommendation Engine".

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1. Type your input

2. See the result

Running algorithm...

3. Use this algorithm

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/paranoia/FpGrowth/0.2.0
View cURL Docs
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://paranoia/FpGrowth/0.2.0 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://paranoia/FpGrowth/0.2.0");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://paranoia/FpGrowth/0.2.0")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://paranoia/FpGrowth/0.2.0")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://paranoia/FpGrowth/0.2.0")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('paranoia/FpGrowth/0.2.0')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("paranoia/FpGrowth/0.2.0")
result <- algo$pipe(input)$result
print(result)
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('paranoia/FpGrowth/0.2.0')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("paranoia/FpGrowth/0.2.0");
let response = algo.pipe(input);
View Rust Docs
import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "paranoia/FpGrowth/0.2.0") { resp, error in
  print(resp)
}
View Swift Docs
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