2.4. Mining Frequent Patterns by Exploring Vertical Data Format

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From the course by University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining

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University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining

178 ratings

Course 4 of 6 in the Specialization Data Mining

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

From the lesson

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

Meet the Instructors

Jiawei Han
Abel Bliss Professor
Department of Computer Science

[SOUND] Now

we are going to look at another interesting pattern mining method.

It's Mining Frequent Patters by Exploring Vertical Data Format.

This method is called ECLAT, or equivalence class transformation method,

which in our philosophy looked like this, okay?

In original transaction database,

it's horizontal data format in the sense you get every row

you get transaction IDs and a set of items in this item entry.

Then you can transform this horizontal data format

into vertical data format like this.

For every item, a,

you will see which transaction IDs is associated with this item a.

That means a bot, you reach transactions.

What's the benefit of this?

The first thing is, you transform the Itemset into TidList.

The total size is approximately the same if every entry or

every ID, they have the similar number of bytes, but

the way to compute this will be different with this TidList.

For example, if you say what is TidList of e?

You get 10, 20, and 30.

What is TidList of a?

You get a 10 and 20.

Then how to derive ae?

You just intersect them together, you can see you intersect this set with this set,

intersect two TidList, you derive the TidList of this ae, this Itemset.

If this one contains sufficient number of transactions,

then this is frequent like two, then ae or be frequent.

If this one's infrequent, then you don't need ae to go further,

that's a similar thing as Apriori principle.

Then the properties of TidLists basically say if these two TidList

is equivalent, that means they have the same set of transactions.

If this one is the subset of the other one, that simply says the transaction

containing X must always containing Y, because this one is this one's subset.

So you probably can see,

if we try to derive frequent patterns based on these vertical intersection,

we just need to see the size of these transaction list.

But there's one interesting method called diffset to accelerate the mining.

The reason is, when you get very large number of transactions,

each item may be associated with a very long TidList.

Then,their intersection, like this e and the ce,

their intersection could be small, but their difference could be small.

So you look at the intersection, it's large, the difference could be small.

For example, you intersect these two, the intersection is 10 and 20.

But the difference is only one.

So you only keep this, you don't have to keep t(ce).

That may save a lot of space, okay?

This is the general idea.

Using diffset you can further improve this efficiency.

[MUSIC]

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