《人工智能与数据挖掘教学课件》l
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人工智能与数据挖掘教学课件-2.datawarehouse
Historical information with time dimension
Data are added without change
Data Mart
A data mart is a small, single-subject data warehouse subset that provides decision support to a small group of people.
The Data Warehouse is always growing.
Operational Database vs. Data warehouse
Operational DB
Similar data can have different representations or meanings
transformation and aggregation, such as data enhancement and mapping, DBMS load scripts, and aggregate definitions
audit, job logs and documentation, such as data lineage records, data transform logs
source descriptive information, such as ownership descriptions, update frequencies and access methods
process information, such as job schedules and extraction code
The Needs for Business metadata
Data are added without change
Data Mart
A data mart is a small, single-subject data warehouse subset that provides decision support to a small group of people.
The Data Warehouse is always growing.
Operational Database vs. Data warehouse
Operational DB
Similar data can have different representations or meanings
transformation and aggregation, such as data enhancement and mapping, DBMS load scripts, and aggregate definitions
audit, job logs and documentation, such as data lineage records, data transform logs
source descriptive information, such as ownership descriptions, update frequencies and access methods
process information, such as job schedules and extraction code
The Needs for Business metadata
人工智能与数据挖掘教学课件-2.datawarehouse 共75页
Star Schema
The star schema is a data modeling technique used to map multidimensional decision support into a relational database.
Ralph Kimball, The Data Warehouse Lifecycle Toolkit, Wiley, 2019, ISBN 0-
471-25547-5
source system metadata
source specifications, such as repositories, and source logical schemas
Data Warehouse environment
the source systems from which data is extracted
the tools used to extract data for loading the data warehouse
the data warehouse database itself where the data is stored
Data Mart
Data Marts can serve as a test vehicle for companies exploring the potential benefits of Data Warehouses.
Data Marts address local or departmental problems, while a Data Warehouse involves a company-wide effort to support decision making at all levels in the organization.
《人工智能与数据挖掘教学课件》courseintro-13.ppt
40
20
0
East Coast
South
Q1 Q2 Q3
Midwest West Coast
Insight
Based on:
Michael J. A. Berry, Data Miners,
2021/3/11
MonAtIh&s DaMs :CBUuPsTtomer
✓ Approach – give away gifts: target customers, what gift, what time
2021/3/11
AI & DM: BUPT
5
Issues to consider: 1. Targeting customers
• Every customer • High expenditure customers • Most profitable customers (who are) • Customers likely to churn (concentrate on the ones
– There are different types of information systems that can support the operation of business: word processor, spread sheets, databases, accounting systems, ERP, decision support systems, expert systems, business intelligence…
4
Example: why CRM needs DM
✓ CRM for mobile phone company – customer retention (churn)
人工智能(知识工程和数据挖掘)课件
首先收集最初的信息（系统没有启动）， 根据其作出推断。
然后，收集另外的信息（电源良好、电线 没有问题）。
最终确定导致故障的原因。
9.2 专家系统可以解决什么问题？
Rule: 1 if then
Rule: 2 if and then
Rule: 3 if and and then
如何验证结果？ 为了验证结果可以使用没有遇到过的例子
集。在训练前，将所有可用的数据随机分 成训练集和测试集，可以用测试集进行测 试 神经网络是不透明的，要想把握输入输出 之间的关系，可以通过灵敏度分析 执行灵敏度分析要将每个输入设成最小值 ，然后再设成最大值，并测量网络的输出
9.5 遗传算法可以解决的问题
task is ‘system start-up’ ask problem
task is ‘system start-up’ problem is ‘system does not start’ ask ‘test power cords’
task is ‘system start-up’ problem is ‘system does not start’ ‘test power cords’ is ok ask ‘test Powerstrip’
9.5 遗传算法可以解决的问题
遗传算法是怎么解决TSP问题的? 首先，要决定如何表达推销员的路线。最
自然的方法就是路径表示法。每个城市用 字母或数字命名，城市间的路线用染色体 来表示，用合适的遗传操作来产生新的路 线
9.5 遗传算法可以解决的问题
TSP中的交叉操作如何进行 传统形式的交叉操作不能直接在TSP中使
9.6 混合智能系统可以解决的问题
可以训练后向传送神经网络来SPECT图 像分成正常图像和异常图像吗？
然后，收集另外的信息（电源良好、电线 没有问题）。
最终确定导致故障的原因。
9.2 专家系统可以解决什么问题？
Rule: 1 if then
Rule: 2 if and then
Rule: 3 if and and then
如何验证结果？ 为了验证结果可以使用没有遇到过的例子
集。在训练前，将所有可用的数据随机分 成训练集和测试集，可以用测试集进行测 试 神经网络是不透明的，要想把握输入输出 之间的关系，可以通过灵敏度分析 执行灵敏度分析要将每个输入设成最小值 ，然后再设成最大值，并测量网络的输出
9.5 遗传算法可以解决的问题
task is ‘system start-up’ ask problem
task is ‘system start-up’ problem is ‘system does not start’ ask ‘test power cords’
task is ‘system start-up’ problem is ‘system does not start’ ‘test power cords’ is ok ask ‘test Powerstrip’
9.5 遗传算法可以解决的问题
遗传算法是怎么解决TSP问题的? 首先，要决定如何表达推销员的路线。最
自然的方法就是路径表示法。每个城市用 字母或数字命名，城市间的路线用染色体 来表示，用合适的遗传操作来产生新的路 线
9.5 遗传算法可以解决的问题
TSP中的交叉操作如何进行 传统形式的交叉操作不能直接在TSP中使
9.6 混合智能系统可以解决的问题
可以训练后向传送神经网络来SPECT图 像分成正常图像和异常图像吗？
《人工智能与数据挖掘教学课件》lect-5-13
d (i, x |2 ) i1 j1 i2 j2 ip jp
– d(i,i) = 0
– d(i,j) = d(j,i) – d(i,j) d(i,k) + d(k,j)
2019/1/28 AI&DM BUPT 16
– Calculate the standardized measurement (z-score)
xif m f zif sf
2019/1/28 AI&DM BUPT 18
4.2 Binary Variables (二值变量)
• A contingency table （相依表）for binary data
where i = (xi1, xi2, …, xip) and j = (xj1, xj2, …, xjp) are two p-dimensional data objects, and q is a positive integer
2019/1/28 AI&DM BUPT 17
4.1 Interval-valued variables (cont. 2)
Object j
1
Object i
0 b d
sum a b cd p
1 0
a c
sum a c b d
• Simple matching coefficient (if the binary variable is
symmetric （对称的）):
d (i, j)
bc a bc d bc a bc
2019/1/28
AI&DM BUPT
4
Example
Price($)
7 20 22 50 51 53
《人工智能与数据挖掘教学课件》2.datawarehouse-文档资料
Data Warehouse environment
the source systems from which data is extracted the tools used to extract data for loading the data warehouse the data warehouse database itself where the data is stored the desktop query and reporting tools used for decision support
Data Warehousing Process Overview
Operational Vs. Multidimensional View Of Sales
Hale Waihona Puke Creating A Data Warehouse
The Data Warehouse
The Data Warehouse is an integrated, subject-oriented, time-variant, nonvolatile database that provides support for decision making.
The Data Warehouse
Integrated
The Data Warehouse is a centralized, consolidated database that integrates data retrieved from the entire organization. The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company.
人工智能(六)知识发现与数据挖掘ppt课件
人工智能 Artificial Intelligence
北京信息科技大学计算机学院 李宝安
精选ppt课件
1
知识发现与数据挖掘
精选ppt课件
2
数据库技术和计算机网络已经成为当前计 算机应用中的两个最重要的基础领域，触及到 人类生活的各个方面。目前，全世界数据库和 因特网中的数据总量正以极快的速度增长。虽 然简单的数据查询或统计可以满足某些低层次 的需求，但人们更为需要的是从大量数据资源 中挖掘出对各类决策有指导意义的一般知识。 数据的急剧膨胀和时效性、复杂性远远超过了 人们的手工处理能力，人们迫切需要高性能的 自动化数据分析工具，以高速、全面、深入、 有效地加工数据。
B
8.67
3.571 2.427 21.038 51.06
C
14.00
7.155
1.957 7.395
53.61
D
24.67 16.889 1.418 36.459 53.89
精选ppt课件
13
BACON4调用上述的启发式，寻到了D和P的单调趋势 关系，即P随D增大而增大，但相应的斜率项不是常数， 而是随D的增加而减少。这又导致BACON4定义D2/P， 此项的值也不是常数，但随D/P减少而增加，结果系统 考虑项D3/P2，这个值接近常数（系统给出了一个允许 的误差范围如7.5％）。BACON4根据这结果就归纳出 该定律了。 一旦一个推理项定义后，它和直接观察的变量就 没有区别了。例如，理想气体定律例中，趋势探测器 会首先确定如PV这样的推理项,并进而确定如PV/T那样 的推理项。也可以发现这些推理项所取值之间的关系， 又从中重新派生出新的推理项，导致对直接观察的变 量更为复杂的描述如PV/nT。BACON4递归地应用相同 的启发式逐步生成更复杂的高层次描述，这种推理能 力使系统具备相当强大的搜索经验定律的功能。
北京信息科技大学计算机学院 李宝安
精选ppt课件
1
知识发现与数据挖掘
精选ppt课件
2
数据库技术和计算机网络已经成为当前计 算机应用中的两个最重要的基础领域，触及到 人类生活的各个方面。目前，全世界数据库和 因特网中的数据总量正以极快的速度增长。虽 然简单的数据查询或统计可以满足某些低层次 的需求，但人们更为需要的是从大量数据资源 中挖掘出对各类决策有指导意义的一般知识。 数据的急剧膨胀和时效性、复杂性远远超过了 人们的手工处理能力，人们迫切需要高性能的 自动化数据分析工具，以高速、全面、深入、 有效地加工数据。
B
8.67
3.571 2.427 21.038 51.06
C
14.00
7.155
1.957 7.395
53.61
D
24.67 16.889 1.418 36.459 53.89
精选ppt课件
13
BACON4调用上述的启发式，寻到了D和P的单调趋势 关系，即P随D增大而增大，但相应的斜率项不是常数， 而是随D的增加而减少。这又导致BACON4定义D2/P， 此项的值也不是常数，但随D/P减少而增加，结果系统 考虑项D3/P2，这个值接近常数（系统给出了一个允许 的误差范围如7.5％）。BACON4根据这结果就归纳出 该定律了。 一旦一个推理项定义后，它和直接观察的变量就 没有区别了。例如，理想气体定律例中，趋势探测器 会首先确定如PV这样的推理项,并进而确定如PV/T那样 的推理项。也可以发现这些推理项所取值之间的关系， 又从中重新派生出新的推理项，导致对直接观察的变 量更为复杂的描述如PV/nT。BACON4递归地应用相同 的启发式逐步生成更复杂的高层次描述，这种推理能 力使系统具备相当强大的搜索经验定律的功能。
人工智能与数据挖掘教学课件-2.datawarehouse
Subject-Oriented
The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company.
What is Data Warehouse
The idea of a data warehouse is to put a wide range of operational data from internal and external sources into one place so it can be better utilized by executives, line of business managers and other business analysts.
The Data Warehouse
Time Variant
The Warehouse data represent the flow of data through time. It can even contain projected data.
Non-Volatile
Once data enter the Data Warehouse, they are never removed.
The Data Warehouse
The Data Warehouse is an integrated, subject-oriented, time-variant, nonvolatile database that provides support for decision making.
The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company.
What is Data Warehouse
The idea of a data warehouse is to put a wide range of operational data from internal and external sources into one place so it can be better utilized by executives, line of business managers and other business analysts.
The Data Warehouse
Time Variant
The Warehouse data represent the flow of data through time. It can even contain projected data.
Non-Volatile
Once data enter the Data Warehouse, they are never removed.
The Data Warehouse
The Data Warehouse is an integrated, subject-oriented, time-variant, nonvolatile database that provides support for decision making.
《人工智能与数据挖掘教学课件》2.datawarehouse.ppt
Once the information is gathered, OLAP (on-line analytical processing ) software comes into play by providing the desktop analysis tools for querying, manipulating and reporting the data from the data warehouse.
The Data Warehouse is always growing.
Operational Database vs. Data warehouse
Operational DB
Data Warehouse
Similar data can have Unified view of all
different representations data elements
Data Warehouse
Why Data warehouse
The most common issue companies face when looking at data mining is that the information is not in one place.
The biggest challenge business analysts face in using data mining is how to extract, integrate, cleanse, and prepare data to solve their most pressing business problems.
Data Mart
Data Marts can serve as a test vehicle for companies exploring the potential benefits of Data Warehouses.
The Data Warehouse is always growing.
Operational Database vs. Data warehouse
Operational DB
Data Warehouse
Similar data can have Unified view of all
different representations data elements
Data Warehouse
Why Data warehouse
The most common issue companies face when looking at data mining is that the information is not in one place.
The biggest challenge business analysts face in using data mining is how to extract, integrate, cleanse, and prepare data to solve their most pressing business problems.
Data Mart
Data Marts can serve as a test vehicle for companies exploring the potential benefits of Data Warehouses.
《人工智能与数据挖掘教学课件》2.datawarehouse名师教学资料
different representations data elements
or meanings
Subject orientation
Functional or process orientation
for decision support
Historical information with time dimension
source descriptive information, such as ownership descriptions, update frequencies and access methods
process information, such as job schedules and extraction code
Once the information is gathered, OLAP (on-line analytical processing ) software comes into play by providing the desktop analysis tools for querying, manipulating and reporting the data from the data warehouse.
BPM: Business performance management BAM: Business activity monitoring PLM: product lifecycle management KMS: Knowledge management systems
Metadata
Automating the meta data management process and enabling the sharing of this so-
人工智能与数据挖掘教学课件-lect-3-12
no
George Professor
5
yes
Joseph Assistant Prof 7
yes
(Jeff, Professor, 4)
Tenured?
1 Example (1): Training Dataset
age income student credit_rating
An
<=30 high <=30 high
– This set of examples is used for model construction: training set
– The model can be represented as classification rules, decision trees, or mathematical formulae
• Note: Test set is independent of training set, otherwise over-fitting will occur
• 2. Model usage: use the model to classify future or unknown
objects
yes
>40 credit rating?
no
yes
excellent fair
no
yes
no
yes
2019/11/26
AI&DM
6
2 Algorithm for Decision Tree Building
• Basic algorithm (a greedy algorithm)
– Tree is constructed in a top-down recursive divide-and-conquer manner
《人工智能与数据挖掘教学课件》2.datawarehou
The Data Warehouse is always growing.
Operational Database vs. Data warehouse
Operational DB
Similar data can have different representations or meanings
The Data Warehouse
Integrated
The Data Warehouse is a centralized, consolidated database that integrates data retrieved from the entire organization.
the desktop query and reporting tools used for decision support
Data Warehousing Process Overview
Operational Vs. Multidimensional View Of Sales
Creating A Data Warehouse
Data Warehouse environment
the source systems from which data is extracted
the tools used to extract data for loading the data warehouse
the data warehouse database itself where the data is stored
The Data Warehouse
Time Variant
The Warehouse data represent the flow of data through time. It can even contain projected data.
Operational Database vs. Data warehouse
Operational DB
Similar data can have different representations or meanings
The Data Warehouse
Integrated
The Data Warehouse is a centralized, consolidated database that integrates data retrieved from the entire organization.
the desktop query and reporting tools used for decision support
Data Warehousing Process Overview
Operational Vs. Multidimensional View Of Sales
Creating A Data Warehouse
Data Warehouse environment
the source systems from which data is extracted
the tools used to extract data for loading the data warehouse
the data warehouse database itself where the data is stored
The Data Warehouse
Time Variant
The Warehouse data represent the flow of data through time. It can even contain projected data.
《人工智能与数据挖掘教学课件》lect-7-13-31页精品文档
l j
l i
2 j
2 i
3 j
3 i
jk
ik
0 . 2 0 0 . 1 0 0 . 3 0 – 0 . 1 0 – 0 . 1 0 0 . 2 0 0 . 1 0 0 . 5 0
Input Layer
1.0
Node 1
W1j
W1i
W2j
0.4
Node 2
W2i
W3j
0.7
Node 3
W3i
Hidden Layer
3. Most popular ANN - Backpropagation Network (8.5.1 The Backpropagation Algorithm: An example)
2019/9/28
AI & DM
2
1. What & Why ANN: Artificial Neural Networks (ANN)
1. What & Why ANN (8.1 Feed forward Neural Network)
2. How ANN works - working principle (8.2.1 Supervised Learning)
3. Most popular ANN - Backpropagation Network (8.5.1 The Backpropagation Algorithm: An example)
– Step 6: Deploy developed network application if the test accuracy is acceptable
2019/9/28
AI & DM
9
《人工智能教程》教学课件 第十章 数据挖掘与Agent技术
• 对遗传算法，还需要进一步研究其数学基础理论；还需要在 理论上证明它与其它优化技术的优劣及原因；还需研究硬件 化的遗传算法；以及遗传算法的通用编程和形式等。
21
10.1.4 数据挖掘的模型与算法
5. 邻近算法
• 邻近算法是一种将数据集合中每一个记录进行分类的方法。这种 分类方式是通过查询已知类似的例子的情况，来判断新例子与已知例 子是否属于同一类。尽管邻近算法存在许多变种，但其一般思路是:
AB规则可信 A与 度 B A同 出时 现出 的现 频的 1率 0频 0%率
• 数据关联支持度：该关联在数据库中出现的频率。 相关例子参见教材
11
10.1.3 数据挖掘的功能与作用
• 聚类也可以称为无监督分类（不需要训练集）。聚类是把一组个 体按照相似性归成若干类别，即“物以类聚”。使得属于同一类 别的个体之间的距离尽可能的小而不同类别上的个体间的距离尽 可能的大。
10
10.1.3 数据挖掘的功能与作用
• 若两个或多个变量的取值之间存在某种规律性，就称为关联。 关联分析的目的就是找出数据库中隐藏的关联规则或关联网。 关联规则可记为AB，A称为前提或左部（LHS），B称为后续 或右部（RHS）。利用数据挖掘的关联分析功能所发现的规则 性知识往往带有可信度。
• 关联规则可信度：
• 数据库中的数据常有一些异常记录，从数据库中检测这些偏差很有意 义。偏差包括很多潜在的知识，如分类中的反常实例、不满足规则的 特例、观测结果与模型预测值的偏差、量值随时间的变化等。偏差分 析的基本方法是，寻找观测结果与参照值之间有意义的差别。
14
10.1.3 数据挖掘的功能与作用
• 数据演变分析描述行为随时间变化的对象的规律或趋势，并对其进 行建模。演变分析也称时间序列分析，可以用变量过去的值来预测未 来的值。 • 演变分析采用的方法一般是在连续的时间流中截取一个时间窗口 （一个时间段），窗口内的数据作为一个数据单元，然后让这个时间 窗口在时间流上滑动，以获得建立模型所需要的训练集。
21
10.1.4 数据挖掘的模型与算法
5. 邻近算法
• 邻近算法是一种将数据集合中每一个记录进行分类的方法。这种 分类方式是通过查询已知类似的例子的情况，来判断新例子与已知例 子是否属于同一类。尽管邻近算法存在许多变种，但其一般思路是:
AB规则可信 A与 度 B A同 出时 现出 的现 频的 1率 0频 0%率
• 数据关联支持度：该关联在数据库中出现的频率。 相关例子参见教材
11
10.1.3 数据挖掘的功能与作用
• 聚类也可以称为无监督分类（不需要训练集）。聚类是把一组个 体按照相似性归成若干类别，即“物以类聚”。使得属于同一类 别的个体之间的距离尽可能的小而不同类别上的个体间的距离尽 可能的大。
10
10.1.3 数据挖掘的功能与作用
• 若两个或多个变量的取值之间存在某种规律性，就称为关联。 关联分析的目的就是找出数据库中隐藏的关联规则或关联网。 关联规则可记为AB，A称为前提或左部（LHS），B称为后续 或右部（RHS）。利用数据挖掘的关联分析功能所发现的规则 性知识往往带有可信度。
• 关联规则可信度：
• 数据库中的数据常有一些异常记录，从数据库中检测这些偏差很有意 义。偏差包括很多潜在的知识，如分类中的反常实例、不满足规则的 特例、观测结果与模型预测值的偏差、量值随时间的变化等。偏差分 析的基本方法是，寻找观测结果与参照值之间有意义的差别。
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10.1.3 数据挖掘的功能与作用
• 数据演变分析描述行为随时间变化的对象的规律或趋势，并对其进 行建模。演变分析也称时间序列分析，可以用变量过去的值来预测未 来的值。 • 演变分析采用的方法一般是在连续的时间流中截取一个时间窗口 （一个时间段），窗口内的数据作为一个数据单元，然后让这个时间 窗口在时间流上滑动，以获得建立模型所需要的训练集。
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2. How ANN: working principle (I)
– Step 1: Collect data
– Step 2: Separate data into training and test sets for network training and validation respectively
3. Most popular ANN Backpropagation Network (8.5.1 The Backpropagation Algorithm: An example)
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1. What & Why ANN: Artificial Neural Networks (ANN)
• ANN is an information processing technology that emulates a biological neural network.
– Neuron (神经元) vs Node (Transformation) – Dendrite (树突) vs Input – Axon (轴突) vs Output – Synapse (神经键) vs Weight
Axon (output wire)
W eight W 1,2
Neuron #2
Dendrite
Axon
Synapse (control of flow of electrochemical fluids
Data signals
Neuron #3
FIGURE Three Interconnected Artificial Neurons
Output Layer
Node j Node i
Wjk
Node k
Wik
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What is ANN: Basics
– Types of ANN
• Network structure, e.g. Figure 17.9 & 17.10 (Turban, 2000, version 5, p663)
and estimation
• multi-layer: Input layer, Hidden layer(s), Output layer
• Fully connected
• Feed forward
• Error back-propagation
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Content
1. What & Why ANN (8.1 Feed forward Neural Network)
• Starts in 1970s, become very popular in 1990s, because of the advancement of computer technology.
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Input data
Dendrite input wire
Neuron #1
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T a b l e 8 .1 • I n i t i a lW e i g h t V a l u e s f o r t h e N e u r a lN e t w o r k S h o w n i n F i g u r e 8 .1
WWW W W W W W
l j
2. How ANN works - working principle (8.2.1 Supervised Learning)
3. Most popular ANN Backpropagation Network (8.5.1 The Backpropagation Algorithm: An example)
– Step 3: Select network structure, learning algorithm, and parameters
• Set the initial weights either by rules or randomly • Rate of learning (pace to adjust weights) • Select learning algorithm (More than a hundred
– The ultimate objectives of training: obtain a set of weights that makes all the
instances in the training data predicted as correctly as possible.
– Back-propagation is one type of ANN which can be used for classification
• Number of hidden layers
• Number of hidden nodes
• Feed forward and feed backward (time dependent problems)
• Links ቤተ መጻሕፍቲ ባይዱetween nodes (exist or absent of links)
Part III: Advance Data Mining Techniques
Chapter 8 Neural Networks
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Content
1. What & Why ANN (8.1 Feed forward Neural Network)
2. How ANN works - working principle (8.2.1 Supervised Learning)
learning algorithms available for various situations and configurations)
l i
2 j
2 i
3 j
3 i
jk
ik
0 . 2 0 0 . 1 0 0 . 3 0 – 0 . 1 0 – 0 . 1 0 0 . 2 0 0 . 1 0 0 . 5 0
Input Layer
1.0
Node 1
W1j
W1i
W2j
0.4
Node 2
W2i
W3j
0.7
Node 3
W3i
Hidden Layer