[PPT] - 1 Relational Database Domains SNOOPYFAMILY PowerPoint Presentation

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資料庫系統實驗室

指導教授：張玉盈

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Relational Database

ID NAME SEX 1 SNOOPY Male 2 CHARLIE BROWN Male 3 SALLY BROWN Female 4 LUCY VAN PELT Female 5 LINUS VAN PELT Male 6 PEPPERMINT PATTY Female 7 MARCIE Female 8 SCHROEDER Male 9 WOODSTOCK

Degree

Cardinality Attributes Tuples

Male Female

Domains Primary Key

Select NAME From SNOOPYFAMILY Where SEX = ‘Male’; ❖ 利用SQL做查詢： ❖ 結果：

ID NAME SEX

1 SNOOPY Male 2 CHARLIE BROWN Male 5 LINUS VAN PELT Male 8 SCHROEDER Male

SNOOPYFAMILY

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Introduction

Data mining is widely used to mine or extract

previously unknown, hidden, and potentially useful information from the large database.

Frequent pattern mining is a basic research topic in

pattern mining.

It generates all frequent patterns with no smaller

supports (or frequency) than a given minimum support threshold.

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{A} {B} {C} {D} {E} 2 3 3 1 3 Itemset Sup. C1 {A} {B} {C} {E} 2 3 3 3 Itemset Sup. L1 Scan D Scan D Scan D {A B} {A C} {A E} {B C} {B E} {C E} Itemset C2 {B C E} Itemset C3 {A B} {A C} {A E} {B C} {B E} {C E} Itemset C2 1 2 1 2 3 2 Sup. {A C} {B C} {B E} {C E} Itemset L2 2 2 3 2 Sup. {B C E} Itemset C3 Sup. 2 {B C E} Itemset L3 Sup. 2 100 200 300 400 A C D B C E A B C E B E TID Items Database D

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Frequent Pattern Mining

This technique has two limitations.
First, it only considers that each item exists or does

not exist in the binary form.

Second, all items have same value with the same

importance.

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Data Mining

Data mining is the process of finding hidden and useful knowledge

form the large databases.

However, items have different importance in the real world.
For example, the iPhone (cellphone) is expensive and the telephone is

cheap.

Therefore, we have to consider the importance and the count of the

items at the same time.

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Mining Weight Maximal Frequent Patterns

User wants to know which pattern can make money and the most items.

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Example

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Item Weight A 0.6 B 0.8 C 0.4 TID Transaction 1 A, C 2 B, C 3 A, B, C 4 A, B 5 B, C

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Frequent Itemsets

We can use the Apriori algorithm to find frequent

patterns.

𝑀1={A,B,C}
𝑀1=>𝐷2
𝐷2={AB,AC,BC}
𝑀2={AB,AC,BC}
𝑀2=>𝐷3
𝐷3={ABC}
𝑀3= ø

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{A,B,C}:1 {A,C}:2 {B,C}:3 {A,B}:2 {A}:3 {B}:4 {C}:4 {item set}:count Min_Sup:2

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Weighted Frequent pattern

Item Weight A 0.6 B 0.8 C 0.4

{item set}:WSup Min_Sup:1.8

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{A,B,C}:0.6 {A,C}:1.0 {A,B}:1.4 {A}:1.8 {B}:3.2 {C}:1.6 {B,C}:1.8 WSup(PS) = sup(PS)*

σ𝑗=1

𝑚𝑓𝑜𝑕𝑢ℎ(𝑄𝑇)(𝑄𝑗)

𝑚𝑓𝑜𝑕𝑢ℎ(𝑄𝑇)

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In this case, the weighted frequent patterns are {A}, {B}, {B,C}.
The weighted maximal frequent pattern is a pattern which does not

have any weighted frequent super pattern.

So, the weighted maximal frequent patterns in this case are {A},

{B,C}.

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In the real world, each item has different profit and

the number of items purchased by consumers may be not only one.

In utility mining, each item has internal utility value

that represents the quantity of the item in each transaction, and external utility value such as profit

r price.

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Mining High Utility Patterns

Which itemset can contribute the most profit value of all the transactions?

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In recent years, many applications have generated

stream data such as transactions of retail markets.

These data are continuous, unbounded, and

usually coming with high speed.

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Traditional vs. Stream Data

Traditional Databases
Data stored in finite, persistent data sets.
Stream Data (Big data in cloud)
Data as ordered, continuous, rapid, huge amount, time-varying data streams.

(In-Memory Databases)

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Sliding Window Model

t0 t1

… … …

t2 ti tj tj+1 tj+2 W1 W2 W3 time Figure 2. Sliding Window

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In the sliding window model, only recent data in a

fixed size window are employed to discover meaningful patterns over data streams.

This model is widely used for stream mining because
f its ability to emphasize recent data and requires

bounded memory resources.

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Mining high utility patterns

Problem Statement

Given a data stream and a user-specified minimum

utility threshold, mining high utility patterns in a window over the data stream is equivalent to discover a set of patterns having no smaller utilities than the minimum utility threshold from this window.

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Simple Example

TID Transaction TU T1 (A, 2) (B, 3) (C, 1) 1550 T2 (A, 1) (B, 2) 300 T3 (A, 2) 400 Item Profit A 200 B 50 C 1000

uT(AB, T1) = uT (A, T1) + uT (B, T1) = 200 × 2 + 50 × 3 = 550 u(AB) = uT (AB, T1) + uT (AB, T2) = 550 + 200 × 1 + 50 × 2 = 850 TU(T1) = 200 × 2 + 50 × 3 + 1000 × 1 = 1550 TWU(AB) = TU(T1) + TU(T2) = 1550 + 300 = 1850

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Periodicity Mining in Time Series Databases

Three types of periodic patterns:
Symbol periodicity
T = abd acb aba abc
Symbol a , p = 3, stPos = 0
Sequence periodicity (partial periodic patterns)
T = bbaa abbd abca abbc abcd
Sequence ab, p = 4, stPos = 4
Segment periodicity (full-cycle periodicity)
T = abcab abcab abcab
Segement abcab, p = 5, stPos = 0

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User wants to know whether the pattern periodic or not in the time- series database.

Mining Frequent Periodic Patterns

Use computer to analyze time-series database. Find frequent periodic patterns and predict the future tend of the time- series database. How to earn money?

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Customers buy something, storage item and time-interval.

Mining Time-Interval Sequential Patterns

Use computer to analyze database. Find Time-interval patterns not only reveals the order of items but also the time intervals between successive items.

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知識的表達效率分析處理

資料庫模型、資料結構、資料整體的維護查詢處理、簡單性、回應時間、空間需求查詢語言、使用方便性

圖例. 資料庫系統的研究領域