基于FP-Tree的关联规则FP-Growth推荐算法Java实现

package edu.test.ch8;

import java.util.ArrayList;
import java.util.List;

public class Item implements Comparable {

private String value;
private Item preItem; // 前继节点Item
private List<Item> nextItem = new ArrayList<Item>(); // 后续节点Item

private Item sibling; // 关联节点

private int counter;

public Item() {

}

public Item(String value) {
this.value = value;
}

public void addCounter() {
this.counter += 1;
}

public Item getSibling() {
return sibling;
}

public void setSibling(Item sibling) {
this.sibling = sibling;
}

public void addNextItem(Item item) {
this.nextItem.add(item);
}

public List<Item> getNextItem() {

return this.nextItem;
}

public String getValue() {
return value;
}

public void setValue(String value) {
this.value = value;
}

public Item getPreItem() {
return preItem;
}

public void setPreItem(Item preItem) {
this.preItem = preItem;
}

public int getCounter() {
return counter;
}

public void setCounter(int counter) {
this.counter = counter;
}

public int compareTo(Object o) {
int value;
Item i = (Item) o;

if (this.counter > i.counter) {
value = -1;
} else if (this.counter == i.counter) {
value = 0;
} else {
value = 1;
}
return value;
}

}

package edu.test.ch8;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;

public class FPGrowth {

private int minSup;

/**
* @param args
*/
public static void main(String[] args) {
FPGrowth fpg = new FPGrowth();
fpg.setMinSup(1000);
List<String> data = fpg.buildData("retail.dat");
Item[] f1Items = fpg.buildF1Items(data);

Map<String, List<String>> condBase;
//Item fpTree = fpg.buildFPTree(data, f1Items);
fpg.buildFPTree(data, f1Items);
// fpg.fpGrowth();
/*
fpg.printFPTree(fpTree);
fpg.printF1Items(f1Items);*/
condBase = fpg.buildCondBase(f1Items);
//  fpg.printCondBase(condBase);
Map<String, Item> condFPTree = fpg.buildCondFPTree(condBase);
//  fpg.printCondFPTree(condFPTree);
//输出频繁子集
Map<String, List<List<String>>> fpSetMap = fpg.fpGrowth(condFPTree);
fpg.printFPSet(fpSetMap);

}
/**
* 输出频繁集
*/
public void printFPSet(Map<String, List<List<String>>> fpSetMap){
List<List<String>> fpSet;

Set<String> items = fpSetMap.keySet();
for(String item : items){
System.out.println(item);
fpSet = fpSetMap.get(item);
for (int i = 0; i < fpSet.size(); i++) {
for (String str : fpSet.get(i)) {
//  if(str != null && str.length() > 0){
System.out.print(str + ", ");
//  }

}
System.out.println(item);
}
}
}

// 输出fpTree
public void printFPTree(Item root) {
System.out.print(root.getValue() + ", " + root.getCounter() + " "
+ root.getNextItem().size() + ": ");
List<Item> subItems = root.getNextItem();
if (subItems.size() != 0) {
for (int i = 0; i < subItems.size(); i++) {
printFPTree(subItems.get(i));
}
System.out.println();
}

}

// 输出频繁1项集
public void printF1Items(Item[] f1Items) {
for (Item item : f1Items) {

while ((item = item.getSibling()) != null) {
System.out.print("item: " + item.getValue() + ": "
+ item.getCounter() + " ,");
if (item.getPreItem() != null) {
System.out.println(item.getPreItem().getValue());
}
}
System.out.println();
}
}

// 输出条件模式基
public void printCondBase(Map<String, List<String>> condBaseMap) {

Set<String> items = condBaseMap.keySet();
List<String> conBase;
for (String item : items) {
System.out.print("Item: " + item);
conBase = condBaseMap.get(item);
System.out.println(", " + conBase.size());
for (String str : conBase) {
System.out.println(str + " ");
}
}
}

// 输出条件fp树
public void printCondFPTree(Map<String, Item> condFPTreeMap) {
Set<String> items = condFPTreeMap.keySet();
for (String item : items) {
System.out.println("Item: " + item);
this.printFPTree(condFPTreeMap.get(item));
}
}

/**
* 1.构造数据集
*/
public List<String> buildData(String...fileName) {

List<String> data = new ArrayList<String>();
if(fileName.length !=0){
File file = new File(fileName[0]);
try {
BufferedReader reader = new BufferedReader(new FileReader(file));
String line;
while( (line = reader.readLine()) != null){
data.add(line);
}

} catch (FileNotFoundException e) {

e.printStackTrace();
} catch (IOException e) {

e.printStackTrace();
}
}else{

data.add("I1 I2 I5");
data.add("I2 I4");
data.add("I2 I3");
data.add("I1 I2 I4");
data.add("I1 I3");
data.add("I2 I3");
data.add("I1 I3");
data.add("I1 I2 I3 I5");
data.add("I1 I2 I3");
}
return data;
}

/**
* 2.构造频繁1项列表，同时作为树的项头表
*/
public Item[] buildF1Items(List<String> data) {
List<Item> itemList = new ArrayList<Item>();
Map<String, Item> resultMap = new HashMap<String, Item>();
for (String trans : data) {

String[] items = trans.trim().split(" ");
int i;
for (String item : items) {

if(resultMap.get(item) == null){
Item newItem = new Item();
newItem.setValue(item);
newItem.setCounter(1);
resultMap.put(item, newItem);
}else{
resultMap.get(item).addCounter();
}
}
}
Set<String> keySet = resultMap.keySet();
for(String key : keySet){
itemList.add(resultMap.get(key));
}
List<Item> result = new ArrayList<Item>();
// 把支持度小于minSup的项从列表中删除
for (int i = 0; i < itemList.size(); i++) {
if (itemList.get(i).getCounter() >= this.minSup) {
result.add(itemList.get(i));
}
}

// 对列表进行排序
Item[] f1Items = result.toArray(new Item[0]);
Arrays.sort(f1Items);

return f1Items;
}

/**
* 3. 构造fpTree
*/
public Item buildFPTree(List<String> data, Item[] f1Items) {

Item fpTree = new Item();
List<Item> subItems;
// 对每一条事务进行处理
for (String trans : data) {

// 得出每条事件中涉及的元素项
String[] items = trans.trim().split(" ");
// 对items中的元素按其在频繁1项集中出现次数排序
items = sortItem(items, f1Items);
// 把items的值加入到fpTree中
subItems = fpTree.getNextItem();

if (subItems.size() == 0) {
this.addLeaf(fpTree, items, f1Items);
} else {
Item temp = null;

for (int i = 0; i < items.length; i++) {
int j = 0;
int size = subItems.size();
for (; j < subItems.size(); j++) {
if (subItems.get(j).getValue().equals(items[i])) {
temp = subItems.get(j);
temp.addCounter();
subItems = temp.getNextItem();
break;
}
}

if (j == size) {
if (temp == null) {
this.addLeaf(fpTree, Arrays.copyOfRange(items, i,
items.length), f1Items);
} else {
this.addLeaf(temp, Arrays.copyOfRange(items, i,
items.length), f1Items);
}
break;
}
}
}

}
return fpTree;
}

/**
* 3.1 对元素数组根据其在f1中出面的频繁进行排序
*
* @param items
* @return
*/
public String[] sortItem(String[] items, Item[] f1Items) {

String[] temp = new String[f1Items.length];
int i;
for (String item : items) {
for (i = 0; i < f1Items.length; i++) {
if (item.equals(f1Items[i].getValue())) {
temp[i] = item;
}
}
}
List<String> list = new ArrayList<String>();
int j = 0;
for (i = 0; i < temp.length; i++) {
if (temp[i] != null) {
list.add(temp[i]);
}
}

return list.toArray(new String[0]);
}

/**
* 3.2 给FPTree的节点添加子节点序列
*
* @param preItem
* @param items
*/
public void addLeaf(Item preItem, String[] items, Item[] f1Items) {
if (items.length > 0) {
Item item = new Item(items[0]);
item.setCounter(1);
item.setPreItem(preItem);
preItem.addNextItem(item);

for (Item i : f1Items) {
if (i.getValue().equals(items[0])) {
Item temp = i;
while (temp.getSibling() != null) {
temp = temp.getSibling();
}
temp.setSibling(item);
break;
}
}
if (items.length > 1) {
addLeaf(item, Arrays.copyOfRange(items, 1, items.length),
f1Items);
}
}

}

// 4.生成条件模式基
public Map<String, List<String>> buildCondBase(Item[] f1Items) {

Item item = null; // 横向处理时的当前节点
Item preItem = null; // 横向处理的当前节点对应的纵向节点
int counter = 0;
StringBuffer data;

Map<String, List<String>> condBaseMap = new HashMap<String, List<String>>();
List<String> conditionBase; // 条件模式基
// 逆向遍历频繁1项集(但不需处理其第一项)
for (int i = f1Items.length - 1; i > 0; i--) {

conditionBase = new ArrayList<String>();
item = f1Items[i].getSibling();
while (item != null) { // 横向处理

counter = item.getCounter();
preItem = item.getPreItem();
data = new StringBuffer();
while (preItem.getValue() != null) { // 纵向处理
data.append(preItem.getValue() + " ");
preItem = preItem.getPreItem();
}
for (int j = 0; j < counter; j++) {
if (data.toString().trim() != ""
&& data.toString().trim().length() > 0) {
conditionBase.add(data.toString().trim());
}
}
item = item.getSibling();
}
condBaseMap.put(f1Items[i].getValue(), conditionBase);
}

return condBaseMap;
}

// 5.生成条件FP树
public Map<String, Item> buildCondFPTree(
Map<String, List<String>> condBaseMap) {

Map<String, Item> condFPTreeMap = new HashMap<String, Item>();
List<String> condBase;
Item condFPTree;
Set<String> items = condBaseMap.keySet();
for (String item : items) {
condBase = condBaseMap.get(item);
condFPTree = this
.buildFPTree(condBase, this.buildF1Items(condBase));
// 删除condFPTree树中节点出现次数少于最小支持度的点
this.delLTminSup(condFPTree);
condFPTreeMap.put(item, condFPTree);
}

return condFPTreeMap;
}

/**
* 5.1  删除树中节点计数小于最小支持度的节点
*
* @param root
*/
public void delLTminSup(Item root) {
List<Item> subItems = root.getNextItem();
if (subItems.size() != 0) {
for (int i = 0; i < subItems.size(); i++) {
if (subItems.get(i).getCounter() < this.minSup) {
subItems.remove(i);
} else {
delLTminSup(subItems.get(i));
}
}
}
}

/**
* 6.产生频繁模式 根据前面生成的条件FP树，分别产生相应元素相关的频繁模式
*/
public Map<String,List<List<String>>> fpGrowth(Map<String, Item> condFPTreeMap) {

List<List<String>> result;
Map<String, List<List<String>>> resultMap = new HashMap<String, List<List<String>>>();
Set<String> items = condFPTreeMap.keySet();
Item condFPTree = null;
List<String> pathList; // 一个条件fp树中所有的路径
List<String> stack = new ArrayList<String>();

for (String item : items) {

pathList = new ArrayList<String>();
condFPTree = condFPTreeMap.get(item);
buildPath(stack, condFPTree, pathList);

for(String str : pathList){
result = new ArrayList<List<String>>();
if(str.trim().length() != 0){
String[] temp = str.trim().split(" ");
List<String> nodeList = new ArrayList<String>();
for(String t : temp){
nodeList.add(t);
}

buildSubSet(nodeList, result);

if(resultMap.get(item) == null){
resultMap.put(item, result);
}else{
List<List<String>> list = resultMap.get(item);
for( int  i = 0; i < result.size(); i++){
list.add(result.get(i));
}
resultMap.put(item, list);
}
}
}
}

return resultMap;
}

// 6.1 生成树的每一条路径
public void buildPath(List<String> stack, Item root, List<String> pathList) {

if (root != null) {
stack.add(root.getValue());
if (root.getNextItem().size() == 0) {
changeToPath(stack, pathList); // 把值栈中的值转化为路径
} else {
List<Item> items = root.getNextItem();
for (int i = 0; i < items.size(); i++) {
buildPath(stack, items.get(i), pathList);
}
}
stack.remove(stack.size() - 1);
}
}

/**
* 6.1.1 把值栈中的值转化为路径
*
* @param path
* @param pathList
*/
public void changeToPath(List<String> path, List<String> pathList) {
StringBuffer sb = new StringBuffer();
for (int i = 0; i < path.size(); i++) {
if (path.get(i) != null) {
sb.append(path.get(i) + " ");
}

}
pathList.add(sb.toString().trim());

}
/**
* 6.2 生成子集
* @param sourceSet
* @param result
*/
public void buildSubSet(List<String> sourceSet, List<List<String>> result) {

if (sourceSet.size() == 1) {
List<String> set = new ArrayList<String>();
set.add(sourceSet.get(0));
result.add(set);
} else if (sourceSet.size() > 1) {

buildSubSet(sourceSet.subList(0, sourceSet.size() - 1), result);
int size = result.size();

List<String> single = new ArrayList<String>();
single.add(sourceSet.get(sourceSet.size() - 1));
result.add(single);

List<String> clone;
for (int i = 0; i < size; i++) {
clone = new ArrayList<String>();
for (String str : result.get(i)) {
clone.add(str);
}
clone.add(sourceSet.get(sourceSet.size() - 1));

result.add(clone);
}
}
}
public void setMinSup(int minSup) {
this.minSup = minSup;
}
}