《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》lect-3-12
– There are no remaining attributes for further partitioning – majority voting is employed for classifying the leaf
– There are no samples left
– Reach the pre-set accuracy
4/22/2020
age <=30 <=30 30…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40
income high high high medium low low low medium low medium medium medium high medium
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AI&DM
12
3.2 Rules simplification and elimination
A Rule for the Tree in Figure 3.4
IF Age <=43 & Sex = Male & Credit Card Insurance = No THEN Life Insurance Promotion = No (accuracy = 75%, Figure 3.4)
4/22/2020
AI&DM
9
Attribute Selection by Information Gain
Computation
Class P: buys_computer = “yes”
Class N:
E(age) 5 I (2,3) 4 I (4,0)
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(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
The Data Warehouse
Integrated
The Data Warehouse is a centralized, consolidated database that integrates data retrieved from the entire organization.
The Data Warehouse
The Data Warehouse is an integrated, subject-oriented, time-variant, nonvolatile database that provides support for decision making.
Functional or process orientation
Current transaction
Frequent updating
Data Warehouse
Unified view of all data elements
Subject orientation for decision support
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.
Subject-Oriented
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.
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.
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.
The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company.
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.
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.
《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》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-1-13-文档资料
2019/4/24
BUPT AI&DM
8
Induction-based Learning (基于归纳的学习)
– This is a time that one must speak with data.
– 未来属于运算师 (Super Crunchers《超级运算师》, Ian Ayres, 2009)：日常决策将变得越来越自动化，人 的判断作用将局限于为计算提供数据
• 葡萄酒味道和香味的预测:奥利.阿申费尔特是普林斯顿大学的经 济学家,完全不懂葡萄酒的制作，但可以预测波尔多葡萄酒的价 格基于天气（炎热、干燥的年份酒会非常好），准确率高于葡 萄酒专家 • 本书原计划叫“理论的终结”，后来利用google改书名而不是与出 版社编辑讨论，因为发现用此名点击率高63% • 放贷员曾经收入优厚、职责最大，现在只是呼叫中心的接线员， 重复电脑提示的问题，报酬很低
• (2) Extraction of interesting
implicit, previously unknown and (non-trivial, potentially useful)
information or patterns from data in large databases. (generally accepted)
2019/4/24
BUPT AI&DM
6
• 在过去，上海通用保修问题分析主要依靠简单的纯手 工处理的计算方式，每次只能产生寥寥几篇问题报告。 尽管汽车生产量远不如现在大，但这个耗时费力的分 析周期却在根本上导致了保修成本居高不下。在非自 动操作环境下，从保修索赔出现到找出问题原因平均 要花费6～12个月的时间，且在此间往往还需要借助 于通用全球的支持，解决问题的整个过程也主要建立 在经验分析的基础上。另外，不准确的数据导致上海 通用难以准确预测保修成本，从而合理准备下一周期 的保修预算，导致大量运营资金被占用、现金流降低。 • 采用SAS的保修分析解决方案后，上海通用的保修 分析周期在头6个月里就缩短了70%，有效地降低了 保修成本，实现了该系统使用的预期目标。同时，这 些显著的改善效果帮助上海通用在短短半年内就收回 了保修分析系统所有的软硬件投资，共为公司节省了 1,800万人民币的成本。
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• Number of hidden layers
• Number of hidden nodes
• Feed forward and feed backward (time dependent problems)
• Links between nodes (exist or absent of links)
Output Layer
Node j Node i
Wjk
Node k
Wik
2020/11/2
ppt课件
5
What is ANN: Basics
– Types of ANN
• Network structure, e.g. Figure 17.9 & 17.10 (Turban, 2000, version 5, p663)
– 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
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
learning algorithms available for various situations and configurations)
3. Most popular ANN Backpropagation Network (8.5.1 The Backpropagation Algorithm: An example)
2020/11/2
ppt课件
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1. What & Why ANN: Artificial Neural Networks (ANN)
2020/11/2
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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
• 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
– 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
Part III: Advance Data Mining Techniques
Chapter 8 Neural Networks
2020/11/2
ppt课件
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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)
• Starts in 1970s, become very popular in 1990s, because of the advancement of computer technology.
2020/11/2
ppt课件
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Input data
Dendrite input wire
Neuron #1
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
1.0Leabharlann Node 1W1j
W1i
W2j
0.4
Node 2
W2i
W3j
0.7
Node 3
W3i
Hidden Layer
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)
and estimation
• multi-layer: Input layer, Hidden layer(s), Output layer
• Fully connected
• Feed forward
• Error back-propagation
2020/11/2
ppt课件
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Content
1. What & Why ANN (8.1 Feed forward Neural Network)
2020/11/2
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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