2.3. Extensions or Improvements of Apriori

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来自 University of Illinois at Urbana-Champaign 的课程

Pattern Discovery in Data Mining

178 个评分

University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining

178 个评分

课程 4（共 6 门，Specialization Data Mining ）

这这一课程中，我们将学习数据挖掘的基本概念及其基础的方法和应用，然后深入到数据挖掘的子领域——模式发现中，学习模式发现深入的概念、方法，及应用。我们也将介绍基于模式进行分类的方法以及一些模式发现有趣的应用。这一课程将给你提供学习技能和实践的机会，将可扩展的模式发现方法应用在在大体量交易数据上，讨论模式评估指标，以及学习用于挖掘各类不同的模式、序列模式，以及子图模式的方法。

从本节课中

Module 1

Module 1 consists of two lessons. Lesson 1 covers the general concepts of pattern discovery. This includes the basic concepts of frequent patterns, closed patterns, max-patterns, and association rules. Lesson 2 covers three major approaches for mining frequent patterns. We will learn the downward closure (or Apriori) property of frequent patterns and three major categories of methods for mining frequent patterns: the Apriori algorithm, the method that explores vertical data format, and the pattern-growth approach. We will also discuss how to directly mine the set of closed patterns.

2.1. The Downward Closure Property of Frequent Patterns3:59

2.2. The Apriori Algorithm6:11

2.3. Extensions or Improvements of Apriori7:32

2.4. Mining Frequent Patterns by Exploring Vertical Data Format3:43

2.5. FPGrowth: A Pattern Growth Approach8:04

2.6. Mining Closed Patterns3:49

与讲师见面

Jiawei Han
Abel Bliss Professor
Department of Computer Science

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Since the proposal of the famous Apriori algorithm,

there have been many interesting extensions or improvements in this method.

Let's just examine some of them.

So the first line of work is how

to reduce the number of passes of transaction database scans.

We know that database scanning involve disk accessing which

could be quite expensive.

But if you want to find size 10 frequent item sets.

Likely, you will have to scan this database ten times.

That's quite expensive.

To reduce a number of scans, so there's several publications.

One is called partitioning method, we're going to introduce.

Another one called dynamic itemset counting method.

You will probably notice the first author,

Serge Brin who was a PC student of Stanford university.

The second year, 1998 he actually proposed

one of the most famous algorithm PageRank and set up a Google company.

Okay.

So this another line of the work is how to shrink the number of candidates.

You will generate very large number of candidate sets in many cases.

How to shrink the number of candidate sets to be generated could be a big saving.

Hashing method is interesting one for mining max patterns.

Bayardo also proposed pruning by support lower bounding.

Another interesting method is Toivonen proposed sampling method.

The third item's work is to explore some special data structures.

Some tree projection method using H-tree to do H-miner.

And hypercube decomposition is another interesting method.

We're going to introduce a new tree structure called FP-tree.

Let's look at the first one, partitioning method.

Partitioning method guarantee you only need to scan data twice

before you generate all the frequent item sets no matter for how many case that is.

So they observed a very interesting property called

any itemset potentially frequent in transaction database

must be frequent in at least one of its partitions.

That means suppose you get a big transaction database,

you partition them into k partitions.

If items at X is not frequenting any one of these k partitions,

it cannot be frequent in this global transaction database.

Why?

Before you can see, if you got items at x which is not frequent in the first one.

Not frequent in the second one.

Even not frequent in the case one.

So you add them together.

You add all these support together, you get a global support.

You add all these database together, you get a global database.

You frequently can see if every one is smaller then sigma more times its

size then the finally the global hour also smaller than 6,

sigma times the size of global database.

That's why at least one of this database contain TDB frequent.

So with this operation, you can see this partition method will need only two scans.

The first scan is you partition database into K partition database.

How do you partition them?

Each partition you want them to fit into the main memory.

Why?

Because no matter how you scan them, you will not involve any database I/Os.

So that's a reason you can scan once you put them the database, you can work

on it to derive k frequent items that for no matter for how many case you can get.

Okay, that means you can derive all the local frequent item sets.

Okay.

Then based on this property you can do the second scan like this.

You can say, since any frequent item set in

one of these k transactions can be potentially frequent.

So the global candidate sets is which is frequenting

any one of them, it will be a global candidate item set.

Then, the second scan, just count against each database kind of those

counts of the global candidate sets.

Then you will get their account, you add them together,

you will be able to derive global frequent patterns.

That's why this method is guaranteed to scan the database only twice.

So this is pretty interesting.

Another method I'm going to introduce to you called Direct Hashing and Pruning.

This method is trying to reduce a number of candidate sets

based on hashing and pruning techniques.

The general philosophy can be like this, okay.

The observation is, if the k-itemset is frequent, then in this hashing bucket,

it contains several potential itemsets that must be frequent.

So if the hash count is not frequent, than any one of them cannot be frequent.

The general philosophy like this.

Okay.

Why are you going to derive the frequent one item?

In the mean time you set of the hash entry for

the potential two itemsets but why you set a hash entry rather than count

each one because there could be many, many distinct items.

So the frequent tool items as before, you get a frequent 1,

the candidate 2 could be really, really big.

But if you set a hash entry to get several items,

share 1 hash entry, the whole hash table will not be too big.

Then the interesting thing is, while you derive frequent one itemset,

you also can cut many two itemsets that can not be frequent.

For example suppose our minimum support count is 70.

Then this one has only 35, this one has only 58.

That implies those items, none of those items can be frequent.

Because they share this entry,

their support count is still below the threshold.

So only the second one, this bd, be, de, the support is quite big.

The past support threshold, these could be poked.

This may contain the real candidates sets.

Okay.

That's a reading you can reduce the number of

candidates substantially using this hashing and pruning method.

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