项目中有时候需要用到对数据进行关联分析，比如分析一个小商店中顾客购买习惯.
package com.data.algorithm; import com.google.common.base.Splitter;
import com.google.common.collect.Lists;
import com.google.common.collect.Maps;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory; import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.*; /**
* *********************************************************
* <p/>
* Author: XiJun.Gong
* Date: 2017-01-20 15:06
* Version: default 1.0.0
* Class description：
* <p/>
* *********************************************************
*/ class EOC { private static final Logger logger = LoggerFactory.getLogger(EOC.class);
private Map<String, Integer> fmap; //forward map
private Map<Integer, String> bmap; //backward map
private List<Map<String, Integer>> elements = null; private Integer maxDimension; public EOC(final String pathFile, String separatSeq) { BufferedReader bufferedReader = null;
try {
this.fmap = Maps.newHashMap();
this.bmap = Maps.newHashMap();
this.elements = Lists.newArrayList();
maxDimension = 0;
bufferedReader = new BufferedReader(
new InputStreamReader(
new FileInputStream(pathFile), "UTF-8"));
String _line = null;
Integer keyValue = null, mapIndex = 0;
while ((_line = bufferedReader.readLine()) != null) {
Map<String, Integer> lineMap = Maps.newHashMap();
if (_line.trim().length() > 1) {
if (separatSeq.trim().length() < 1) {
separatSeq = ",";
}
for (String word : Splitter.on(separatSeq).split(_line)) {
word = word.trim();
if (null == (keyValue = fmap.get(word))) {
keyValue = mapIndex++;
}
fmap.put(word, keyValue);
bmap.put(keyValue, word);
lineMap.put(word, keyValue);
}
if (maxDimension < lineMap.size())
maxDimension = lineMap.size();
elements.add(lineMap);
}
}
} catch (Exception e) {
logger.error("读取文件出错 , 错误原因:{}", e);
} finally {
if (bufferedReader != null) {
try {
bufferedReader.close();
} catch (IOException e) {
logger.error("bufferedReader , 错误原因:{}", e);
}
}
}
} public Integer getMaxDimension() {
return maxDimension;
} public float getRateOfSet(Collection<Integer> elementChild) {
float rateCnt = 0f;
int allSize = 1;
for (Map<String, Integer> eMap : elements) {
boolean flag = true;
for (Integer element : elementChild) {
if (null == eMap.get(bmap.get(element))) {
flag = false;
break;
}
}
if (flag) rateCnt += 1;
}
return rateCnt / ((allSize = elements.size()) > 1 ? (float) allSize : 1.0f);
} public Set<Integer> getElements() { return new HashSet<Integer>(fmap.values());
} public Integer queryByKey(String key) {
return fmap.get(key);
} public String queryByValue(Integer value) {
return bmap.get(value);
}
} public class Apriori {
private static final Logger logger = LoggerFactory.getLogger(Apriori.class);
private EOC eoc = null;
private Integer maxDimension;
private final float exp = 1e-4f; public Apriori(final String pathFile, String separatSeq, Integer maxDimension) {
this(pathFile, separatSeq);
this.maxDimension = maxDimension;
} public Apriori(final String pathFile, String separatSeq) {
this.eoc = new EOC(pathFile, separatSeq);
this.maxDimension = this.eoc.getMaxDimension();
} public void work(float confidenceLevel) {
List<Set<Integer>> listElement = null;
ArrayList<Set<Integer>> middleWareElement = null;
Map<Set<Integer>, Float> maps = null;
listElement = Lists.newArrayList();
for (Integer element : this.eoc.getElements()) {
Set<Integer> set = new HashSet<Integer>();
set.add(element);
listElement.add(set);
}
maps = Maps.newHashMap();
middleWareElement = Lists.newArrayList();
for (int i = 1; i < this.maxDimension; i++) {
for (Set<Integer> tmpSet : listElement) {
float rate = eoc.getRateOfSet(tmpSet);
if (confidenceLevel - exp <= rate)
maps.put(tmpSet, rate);
}
System.out.println("+++++++++++第 " + i + " 维度关联数据+++++++++++");
output(maps);
listElement.clear();
middleWareElement.addAll(maps.keySet());
maps.clear();
for (int j = 0; j < middleWareElement.size(); j++) {
Set<Integer> tmpSet = middleWareElement.get(j);
for (int k = j + 1; k < middleWareElement.size(); k++) {
Set<Integer> setChild = middleWareElement.get(k);
for (Integer label : setChild) {
if (!tmpSet.contains(label)) {
Set<Integer> newElement = new HashSet<Integer>(tmpSet);
newElement.add(label);
if (!listElement.contains(newElement)) {
listElement.add(newElement);
break;
}
}
}
}
}
middleWareElement.clear();
}
} public void output(Map<Set<Integer>, Float> maps) {
for (Map.Entry<Set<Integer>, Float> iter : maps.entrySet()) {
for (Integer integer : iter.getKey()) {
System.out.print(eoc.queryByValue(integer) + " ");
}
System.out.println(iter.getValue()*100+"%");
}
}
}
package com.data.algorithm; /**
* *********************************************************
* <p/>
* Author: XiJun.Gong
* Date: 2017-01-17 17:57
* Version: default 1.0.0
* Class description：
* <p/>
* *********************************************************
*/
public class Main {
public static void main(String args[]) {
Apriori apriori = new Apriori("/home/com/src/main/java/com/qunar/data/algorithm/demo.data", ",");
apriori.work(0.5f);
}
}
+++++++++++第 1 维度关联数据+++++++++++
苹果 50.0%
西红柿 75.0%
香蕉 75.0%
矿泉水 75.0%
+++++++++++第 2 维度关联数据+++++++++++
苹果 西红柿 50.0%
西红柿 香蕉 50.0%
西红柿 矿泉水 50.0%
香蕉 矿泉水 75.0%
+++++++++++第 3 维度关联数据+++++++++++
西红柿 香蕉 矿泉水 50.0%