Intelligent computing system based on
pattern recognition and data mining algorithms

Abstract

application and technology

the development direction of the intelligent system

the basic of data mining

the advantages and disadvantages

expert system

intelligent system integrations

learning theory

Artificial intelligence

Machine learning

Data mining technology

statistical analysis

fuzzy logic

pattern recognition

Artificial neural networks

the structure of the general algorithm of data mining

Introduction

第二段

第三段

the dynamic of problem

don't need rely on gradient information

based on population probability search

methods

genetic algorithm

particle swarm algorithm

ant colony algorithm

differential evolution algorithm

cultural algorithm

don't need to sent the initial points

not fall into local extremum

Artificial intelligence

a branch of computer science

applied fields

the point of view of its functions

pattern recognition

neural network

expert system

a comprehensive fringe science

control theory

information technology

calculations

linguistics

...

the cognitive system

deductive reasoning

by symbolic processing

the theory of intelligent computing

mainly based on connectionism

fuzzy mathematics

iterated function system

developing directions

Artificial neural networks

genetic algorithms

evolutionary computation

artificial life

ecological computing

immune information processing

multi-agent systems

etc.

"Collaboration"

distributed artificial intelligence

main research

how to coordinate their knowledge, skills and planning

how to solve multi-objective problem with the effective way

to support collaborative work of large and complex intelligent system or computer design

Chapter 2

intelligent computing system

First paragraph

the several basic techniques

the trends

integration of intelligent technology and expert system

fuzzy logic and neural networks

Electric power system

decision trees

fuzzy systems

collective methods

cloud theory

statistical methods

self-organizing mining technology

the imitation biotechnology

features

self-organizing feature mapping network method

group intelligent

optimized

evolution strategy

evolution objects

unsupervised clustering method

group intelligence algorithm to cluster,

to reduce the knowledge of the data vector group randomly into a two-dimensional plane

solving complex dataset

Search engines

Video monitoring system

Knowledge processing methods

collaborative intelligent computing

main application

the diagnosis of power equipment fault

two core problem

information extraction

uncertainly of information increases

methods

neural network based fault diagnosis

fault diagnosis based on optimization technology

expert system based fault diagnosis

if the information is incomplete

prone to errors

characteristics

nonlinear characteristics

parallel processing ability

robustness

self-organizing learning

for small and medium power system failures is good

genetic algorithm

the problem get into an integer programming problem

understand the focal point of the user

customize web page

motion tracking technology

automatic video retrieval technology

moving object technology

pattern analysis technology

Chapter 3

First paragraph

Recognition behavior

specific things

abstract things

Pattern recognition system

consists

data processing

feature extraction and selection

classification decision making

data acquisition

transformation data from sensors to computer

eliminate the noise

filtering algorithms

effective features

be careful the curse of dimensionality

Pattern recognition method

neural networks

Artificial neural network based pattern recognition

Template matching pattern recognition

Four basic models of fuzzy pattern recognition

Proximity principle

Proximity principle/Synthetic fuzzy set

Maximum membership principle

Synthetic fuzzy set/Synthetic approach degree

A number of identification methods

not developed a unified and effective pattern

neural network

allow pattern to be noisy

EX: BP neural network

Table 2

purpose

develop general data analysis techniques

not rely on application domains

current work

figure out a combination of the specific problems

propose new methods of pattern recognition

it is very difficult to achieve in practice.

the computation of template matching is very large.

mainly used in

tracking of moving objects

detection of object position in images

based on support vector machines

solving

construct an optimal hyper-plane

to maximize the distance

hyper plane

different samples

pattern recognition

function estimation problems

Synergetic pattern recognition

a high-dimensional nonlinear problem

reduced to

a nonlinear equation with the same set of dimensions.

mainly to establish stochastic differential equations.

used to eliminate the stable modulus

to obtain the closed equation of order parameter.

the high-dimensional problem

reduced to

a low dimensional problem .

Compared with the traditional pattern recognition system

reduces the process of feature extraction and selection.

Data mining algorithms

Bayesian algorithm

Timing algorithm

the main methods

Clustering algorithm

Association algorithm

Neural Network algorithm

Regression algorithm

Basic concepts of association rules

A mining algorithm of association rules

Data set partitioning algorithm

Incremental updating algorithm

Parallel mining algorithm

the actual development often turns into formalization of domain knowledge and discovery tasks

Model discovery is a cyclic heuristic process

Decision tree

earliest model

ID3

bigger dataset

IBLE method

Neural network

MP model and the Hebb learning rule

propagation model and functional network

used in

prediction

pattern recognition

Concept tree method

Fuzzy set method

Visualization technology

a classification algorithm

requires a small amount of computation

for predictive

discover the relation between the input column and the predictable column

used for initial data detection

a regression algorithm

product sales in a prediction scheme

to predict continuous columns,

a segmentation algorithm

identifying exceptions

Scatter diagrams

describe how these items are grouped.

依照購客已買的東西預測未來會買的東西

在多層感知器網絡中

每個神經元接收一個或多個輸入並產生一個或多個相同的輸出。每個輸出是神經元輸入總和的簡單非線性函數。

輸入值從輸入層中的節點傳遞到隱藏層中的節點,最後傳遞到輸出層。

輸入圖層,可選的隱藏圖層和輸出圖層

a variant of the decision tree algorithm

completeness

mining frequent itemsets

maximal frequent itemsets

closed frequent itemsets

constrained frequent itemsets

association rule

single layer rule

multi-level associations rule

the types of values

Boolean association rules

quantitative association rules.

the types of mining rules

association rules

related rules mining

mining pattern types

sequential pattern mining

structural pattern mining

frequent itemsets mining

mining constraints

knowledge type constraints

data constraints,

interestingness constraints

rules constraints

the mining algorithms of association rules

In depth first algorithms

breadth first algorithm

data set partitioning algorithm

depth first algorithm

sampling algorithm

Incremental updating algorithm

and so on

hierarchical algorithm

AprioriTid

AprioriHy-brid algorithms

Apriori

DHP

uses hash table technology

no longer scanning the entire database

using the initial scan of the database

FP growth algorithm

OP algorithm

does not need to generate a large set of candidate items

Tree Projection algorithm

但其應用難點在於,在處理大型稀疏數據庫時,挖掘,處理和遞歸計算需要相當大的空間。

Partition算法

DIC算法

包括D.W.Cheung提出的FUP算法,IUA,PIUA,IUAR算法等

FUP算法是Ap​​riori算法的改進,也是解決增量更新問題的經典算法。

FUP算法主要針對在最小支持度和最小置信度保持不變的情況下修改數據庫時如何生成更新數據庫的關聯規則。

它使用挖掘過程獲得的頻繁項集信息,避免重複計算頻繁項集支持數的時間成本,提高算法的效率。

Agrawal等人提出的CD,DD和CaD算法。

Park等人提出的PDM算法。

並行算法使用一組同時進程,交互和協調來完成給定問題的解決方案。

Cheung等人提出的DMA和FDM算法

但它的缺點是流量和候選頻繁項目集都相對較大。

CaD算法試圖通過劃分數據庫和候選集來減少處理器之間的數據依賴性,每個處理器可以獨立計算.

DD算法的缺點,然而由於低效率通信負載的移動解決方案的數據,處理器之間的大交互模式容易導致處理器是 處於空閒狀態,每個事務記錄基於多個哈希樹處理導致冗餘計算.

PDM算法類似 對於CD算法,所有處理器都具有相同的哈希表和候選集。

結論
智能係統集成主要包括人工智能,計算智能方法和其他智能技術。介紹了幾種智能係統集成的應用和技術,以及學習理論和專家系統的優缺點,以及神經網絡在智能係統中的應用。在求解智能計算時,不需要解決問題,或者不需要係統的梯度信息,因此可以獨立處理連續和離散問題。智能計算方法可以較好地解決不同優化問題的全局優化問題的最優解,並且可以利用簡單易懂的啟發式邏輯計算方法簡單地引入智能計算方法。詳細討論了數據挖掘中關聯規則的挖掘,並根據統計數據對一些常用的挖掘算法進行了分析,比較和總結。現有的改進算法無法滿足人們對採礦系統快速,及時響應的需求。因此,我們需要提高采礦過程的效率,並與用戶互動以產生視覺效果。同時,我們介紹了模式識別的各種技術和模型。實例表明,基於模式識別和數據挖掘算法的智能計算系統具有較高的效率和識別率。