This chapter discusses advanced techniques for frequent pattern mining, including mining patterns in multi-level, multi-dimensional datasets (Section 1). It covers mining multi-level associations that allow flexible support thresholds and handle hierarchical relationships between items (Section 2). Multi-dimensional association rule mining is explored, including techniques for categorical and quantitative attributes (Sections 3-4). Additional topics include mining rare and negative patterns, constraint-based pattern mining using queries, and meta-rule guided mining (Sections 5-7).
This chapter discusses advanced techniques for frequent pattern mining, including mining patterns in multi-level, multi-dimensional spaces by considering item hierarchies and multiple dimensions. It also covers constraint-based pattern mining where user-specified constraints are used to direct the mining process. Quantitative association rule mining is discussed, including techniques for discretizing numeric attributes. Mining rare and negative patterns is also addressed.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This document summarizes Chapter 7 of the book "Data Mining: Concepts and Techniques" which covers advanced frequent pattern mining techniques. It discusses mining diverse patterns such as multiple-level, multi-dimensional, quantitative and negative associations. It also covers sequential pattern mining algorithms like GSP, SPADE and PrefixSpan that find frequent subsequences in sequence databases. Finally, it discusses constraint-based pattern mining and applications in software bug detection.
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document discusses Chapter 5 from the book "Data Mining: Concepts and Techniques" which covers frequent pattern mining, association rule mining, and correlation analysis. It provides an overview of basic concepts such as frequent patterns and association rules. It also describes efficient algorithms for mining frequent itemsets such as Apriori and FP-growth, and discusses challenges and improvements to frequent pattern mining.
This document summarizes Chapter 5 of the book "Data Mining: Concepts and Techniques". It discusses frequent pattern mining, which aims to discover patterns that occur frequently in transactional or relational data. It covers basic concepts like support and confidence, and algorithms like Apriori for mining frequent itemsets and generating association rules. It also discusses techniques for improving the efficiency of frequent pattern mining, such as the FP-growth method which avoids candidate generation.
This chapter discusses advanced techniques for frequent pattern mining, including mining patterns in multi-level, multi-dimensional spaces by considering item hierarchies and multiple dimensions. It also covers constraint-based pattern mining where user-specified constraints are used to direct the mining process. Quantitative association rule mining is discussed, including techniques for discretizing numeric attributes. Mining rare and negative patterns is also addressed.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This document summarizes Chapter 7 of the book "Data Mining: Concepts and Techniques" which covers advanced frequent pattern mining techniques. It discusses mining diverse patterns such as multiple-level, multi-dimensional, quantitative and negative associations. It also covers sequential pattern mining algorithms like GSP, SPADE and PrefixSpan that find frequent subsequences in sequence databases. Finally, it discusses constraint-based pattern mining and applications in software bug detection.
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document discusses Chapter 5 from the book "Data Mining: Concepts and Techniques" which covers frequent pattern mining, association rule mining, and correlation analysis. It provides an overview of basic concepts such as frequent patterns and association rules. It also describes efficient algorithms for mining frequent itemsets such as Apriori and FP-growth, and discusses challenges and improvements to frequent pattern mining.
This document summarizes Chapter 5 of the book "Data Mining: Concepts and Techniques". It discusses frequent pattern mining, which aims to discover patterns that occur frequently in transactional or relational data. It covers basic concepts like support and confidence, and algorithms like Apriori for mining frequent itemsets and generating association rules. It also discusses techniques for improving the efficiency of frequent pattern mining, such as the FP-growth method which avoids candidate generation.
This document discusses frequent pattern mining and association rule learning. It begins with basic concepts like frequent itemsets, support, and confidence. It then describes the Apriori algorithm for mining frequent itemsets via candidate generation and testing. Several methods for improving the efficiency of Apriori are also presented, such as partitioning the database to reduce scans, reducing the number of candidates via hashing or sampling, and counting support without candidate generation using pattern growth.
This document discusses frequent pattern mining and association rule learning. It begins with basic concepts like frequent itemsets, support, and confidence. It then describes the Apriori algorithm for mining frequent itemsets via candidate generation and testing. Several methods for improving the efficiency of Apriori are discussed, including partitioning the database, reducing candidate counts using hashing, and sampling. Finally, an alternative pattern-growth approach without candidate generation called FP-Growth is introduced.
Chapter 6. Mining Frequent Patterns, Associations and Correlations Basic Conc...Subrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Mining Frequent Patterns, Association and CorrelationsJustin Cletus
This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.
This chapter discusses frequent pattern mining, which involves finding patterns that frequently occur in transactional or other forms of data. It covers basic concepts like frequent itemsets and association rules. It also describes several algorithms for efficiently mining frequent patterns at scale, including Apriori, FP-Growth, and the ECLAT algorithm. These algorithms aim to address the computational challenges of candidate generation and database scanning.
The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
The document discusses various data mining techniques including association rules, classification, clustering, and approaches to discovering patterns in datasets. It covers clustering algorithms like partition and hierarchical clustering. It also explains different data mining problems like discovering sequential patterns, patterns in time series data, and classification and regression rules.
This chapter discusses frequent pattern mining and association rule mining. It covers basic concepts like frequent itemsets and association rules. It then summarizes efficient and scalable methods for mining frequent itemsets, including Apriori, FP-growth, and the vertical data format approach. The chapter also discusses mining various types of association rules and extending association mining to correlation analysis and constraint-based association mining.
This document provides an introduction to data mining. It discusses why organizations use data mining, such as for credit ratings, fraud detection, and customer relationship management. It describes the data mining process of problem formulation, data collection/preprocessing, mining methods, and result evaluation. Specific mining methods covered include classification, clustering, association rule mining, and neural networks. It also discusses applications of data mining across various industries and gives some examples of successful real-world data mining implementations.
This document provides an introduction to the concept of data mining. It discusses several applications of data mining such as credit ratings, targeted marketing, fraud detection, and customer relationship management. It then defines data mining as the process of analyzing large databases to find valid, novel, useful, and understandable patterns. The document outlines some common data mining techniques including classification, clustering, association rule mining, and collaborative filtering. It provides examples of how these techniques can be applied and discusses their advantages and disadvantages.
Dwdm ppt for the btech student contain basisnivatripathy93
This document provides an introduction to data mining. It discusses why organizations use data mining, such as for credit ratings, fraud detection, and customer relationship management. The document defines data mining as the process of analyzing large databases to find valid, novel, useful, and understandable patterns. It outlines some common data mining applications and techniques, including classification, clustering, association rule mining, and collaborative filtering. The document also compares data mining to related fields and discusses how the knowledge discovery process works.
This document discusses various techniques for mining association rules from transactional databases, including mining frequent itemsets using vertical data formats, mining closed and maximal frequent itemsets, mining quantitative associations using static and dynamic discretization, and constraint-based association rule mining. Key techniques covered include intersecting itemsets' TID sets in vertical format, pruning search space using the Apriori property, and exploiting various constraints like anti-monotonicity to efficiently mine association rules.
It is the concept of Data mining and knowledge discovery in Databases(KDD)..
BIODATA:
I am sameer kumar das working as an asst.professor in CSE at GATE,Odisha and i contd.PhD in Engineering.Thanks
This chapter discusses frequent pattern mining and association rule learning. It begins with basic concepts like frequent patterns, support, and confidence. It then covers several algorithms for mining frequent itemsets, including Apriori, which uses candidate generation and testing, and FP-Growth, which avoids candidates using a tree structure. The chapter concludes by discussing methods for evaluating interesting patterns and scaling the algorithms.
This document discusses association rule mining and frequent pattern mining. It defines association rule mining as finding frequent patterns, associations, correlations, or causal structures among items in transactional databases. An example is provided to illustrate support and confidence for an association rule. Several algorithms are summarized for scalably mining association rules from large transactional databases, including Apriori, Partition, sampling, and FP-growth. FP-growth avoids candidate generation by building an FP-tree structure.
This document discusses frequent pattern mining and association rule learning. It begins by defining frequent patterns as patterns that occur frequently in a dataset. Apriori and FP-Growth are introduced as two popular algorithms for mining frequent itemsets and generating association rules. The document then provides more details on the concepts and implementation of these two algorithms. It explains how Apriori uses a generate-and-test approach with candidate generation while FP-Growth adopts a pattern growth method to avoid candidate generation. Examples are also given to illustrate how each algorithm works step-by-step.
This document provides an overview of association rule mining and algorithms for finding association rules. It discusses the concepts of association rules, including support, confidence and lift. It describes several common algorithms for mining association rules, including Apriori, FP-Growth and ECLAT. It also discusses measures for evaluating the interestingness of discovered rules, including both objective measures like support and confidence as well as subjective user-driven measures.
chapter1_Introduction.pdf data mining pptGyanaKarn
This document provides an introduction to data mining. It discusses the growth of large datasets in commercial and scientific domains. Data mining aims to extract useful knowledge from these large datasets. It can help improve productivity, solve societal problems, and assist scientists with data analysis. The document outlines common data mining tasks like classification, regression, clustering, association rule mining, and anomaly detection. Examples of applications are discussed for tasks like fraud detection, document clustering, and market basket analysis. Overall, the summary introduces the key concepts, goals, techniques and applications of data mining.
This document provides an overview of cluster analysis techniques. It begins by defining cluster analysis and its applications. It then categorizes major clustering methods into partitioning methods (like k-means and k-medoids), hierarchical methods, density-based methods, grid-based methods, and model-based methods. The document discusses different data types that can be clustered and measures for determining cluster quality. It also outlines requirements for effective clustering in data mining.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
This document discusses frequent pattern mining and association rule learning. It begins with basic concepts like frequent itemsets, support, and confidence. It then describes the Apriori algorithm for mining frequent itemsets via candidate generation and testing. Several methods for improving the efficiency of Apriori are also presented, such as partitioning the database to reduce scans, reducing the number of candidates via hashing or sampling, and counting support without candidate generation using pattern growth.
This document discusses frequent pattern mining and association rule learning. It begins with basic concepts like frequent itemsets, support, and confidence. It then describes the Apriori algorithm for mining frequent itemsets via candidate generation and testing. Several methods for improving the efficiency of Apriori are discussed, including partitioning the database, reducing candidate counts using hashing, and sampling. Finally, an alternative pattern-growth approach without candidate generation called FP-Growth is introduced.
Chapter 6. Mining Frequent Patterns, Associations and Correlations Basic Conc...Subrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Mining Frequent Patterns, Association and CorrelationsJustin Cletus
This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.
This chapter discusses frequent pattern mining, which involves finding patterns that frequently occur in transactional or other forms of data. It covers basic concepts like frequent itemsets and association rules. It also describes several algorithms for efficiently mining frequent patterns at scale, including Apriori, FP-Growth, and the ECLAT algorithm. These algorithms aim to address the computational challenges of candidate generation and database scanning.
The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
The document discusses various data mining techniques including association rules, classification, clustering, and approaches to discovering patterns in datasets. It covers clustering algorithms like partition and hierarchical clustering. It also explains different data mining problems like discovering sequential patterns, patterns in time series data, and classification and regression rules.
This chapter discusses frequent pattern mining and association rule mining. It covers basic concepts like frequent itemsets and association rules. It then summarizes efficient and scalable methods for mining frequent itemsets, including Apriori, FP-growth, and the vertical data format approach. The chapter also discusses mining various types of association rules and extending association mining to correlation analysis and constraint-based association mining.
This document provides an introduction to data mining. It discusses why organizations use data mining, such as for credit ratings, fraud detection, and customer relationship management. It describes the data mining process of problem formulation, data collection/preprocessing, mining methods, and result evaluation. Specific mining methods covered include classification, clustering, association rule mining, and neural networks. It also discusses applications of data mining across various industries and gives some examples of successful real-world data mining implementations.
This document provides an introduction to the concept of data mining. It discusses several applications of data mining such as credit ratings, targeted marketing, fraud detection, and customer relationship management. It then defines data mining as the process of analyzing large databases to find valid, novel, useful, and understandable patterns. The document outlines some common data mining techniques including classification, clustering, association rule mining, and collaborative filtering. It provides examples of how these techniques can be applied and discusses their advantages and disadvantages.
Dwdm ppt for the btech student contain basisnivatripathy93
This document provides an introduction to data mining. It discusses why organizations use data mining, such as for credit ratings, fraud detection, and customer relationship management. The document defines data mining as the process of analyzing large databases to find valid, novel, useful, and understandable patterns. It outlines some common data mining applications and techniques, including classification, clustering, association rule mining, and collaborative filtering. The document also compares data mining to related fields and discusses how the knowledge discovery process works.
This document discusses various techniques for mining association rules from transactional databases, including mining frequent itemsets using vertical data formats, mining closed and maximal frequent itemsets, mining quantitative associations using static and dynamic discretization, and constraint-based association rule mining. Key techniques covered include intersecting itemsets' TID sets in vertical format, pruning search space using the Apriori property, and exploiting various constraints like anti-monotonicity to efficiently mine association rules.
It is the concept of Data mining and knowledge discovery in Databases(KDD)..
BIODATA:
I am sameer kumar das working as an asst.professor in CSE at GATE,Odisha and i contd.PhD in Engineering.Thanks
This chapter discusses frequent pattern mining and association rule learning. It begins with basic concepts like frequent patterns, support, and confidence. It then covers several algorithms for mining frequent itemsets, including Apriori, which uses candidate generation and testing, and FP-Growth, which avoids candidates using a tree structure. The chapter concludes by discussing methods for evaluating interesting patterns and scaling the algorithms.
This document discusses association rule mining and frequent pattern mining. It defines association rule mining as finding frequent patterns, associations, correlations, or causal structures among items in transactional databases. An example is provided to illustrate support and confidence for an association rule. Several algorithms are summarized for scalably mining association rules from large transactional databases, including Apriori, Partition, sampling, and FP-growth. FP-growth avoids candidate generation by building an FP-tree structure.
This document discusses frequent pattern mining and association rule learning. It begins by defining frequent patterns as patterns that occur frequently in a dataset. Apriori and FP-Growth are introduced as two popular algorithms for mining frequent itemsets and generating association rules. The document then provides more details on the concepts and implementation of these two algorithms. It explains how Apriori uses a generate-and-test approach with candidate generation while FP-Growth adopts a pattern growth method to avoid candidate generation. Examples are also given to illustrate how each algorithm works step-by-step.
This document provides an overview of association rule mining and algorithms for finding association rules. It discusses the concepts of association rules, including support, confidence and lift. It describes several common algorithms for mining association rules, including Apriori, FP-Growth and ECLAT. It also discusses measures for evaluating the interestingness of discovered rules, including both objective measures like support and confidence as well as subjective user-driven measures.
chapter1_Introduction.pdf data mining pptGyanaKarn
This document provides an introduction to data mining. It discusses the growth of large datasets in commercial and scientific domains. Data mining aims to extract useful knowledge from these large datasets. It can help improve productivity, solve societal problems, and assist scientists with data analysis. The document outlines common data mining tasks like classification, regression, clustering, association rule mining, and anomaly detection. Examples of applications are discussed for tasks like fraud detection, document clustering, and market basket analysis. Overall, the summary introduces the key concepts, goals, techniques and applications of data mining.
This document provides an overview of cluster analysis techniques. It begins by defining cluster analysis and its applications. It then categorizes major clustering methods into partitioning methods (like k-means and k-medoids), hierarchical methods, density-based methods, grid-based methods, and model-based methods. The document discusses different data types that can be clustered and measures for determining cluster quality. It also outlines requirements for effective clustering in data mining.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
5. 5
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Mining Multi-Level Association
Mining Multi-Dimensional Association
Mining Quantitative Association Rules
Mining Rare Patterns and Negative Patterns
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
6. 6
Mining Multiple-Level Association Rules
Items often form hierarchies
Flexible support settings
Items at the lower level are expected to have lower
support
Exploration of shared multi-level mining (Agrawal &
Srikant@VLB’95, Han & Fu@VLDB’95)
uniform support
Milk
[support = 10%]
2% Milk
[support = 6%]
Skim Milk
[support = 4%]
Level 1
min_sup = 5%
Level 2
min_sup = 5%
Level 1
min_sup = 5%
Level 2
min_sup = 3%
reduced support
7. 7
Multi-level Association: Flexible Support and
Redundancy filtering
Flexible min-support thresholds: Some items are more valuable but
less frequent
Use non-uniform, group-based min-support
E.g., {diamond, watch, camera}: 0.05%; {bread, milk}: 5%; …
Redundancy Filtering: Some rules may be redundant due to
“ancestor” relationships between items
milk wheat bread [support = 8%, confidence = 70%]
2% milk wheat bread [support = 2%, confidence = 72%]
The first rule is an ancestor of the second rule
A rule is redundant if its support is close to the “expected” value,
based on the rule’s ancestor
8. 8
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Mining Multi-Level Association
Mining Multi-Dimensional Association
Mining Quantitative Association Rules
Mining Rare Patterns and Negative Patterns
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
9. 9
Mining Multi-Dimensional Association
Single-dimensional rules:
buys(X, “milk”) buys(X, “bread”)
Multi-dimensional rules: 2 dimensions or predicates
Inter-dimension assoc. rules (no repeated predicates)
age(X,”19-25”) occupation(X,“student”) buys(X, “coke”)
hybrid-dimension assoc. rules (repeated predicates)
age(X,”19-25”) buys(X, “popcorn”) buys(X, “coke”)
Categorical Attributes: finite number of possible values, no
ordering among values—data cube approach
Quantitative Attributes: Numeric, implicit ordering among
values—discretization, clustering, and gradient approaches
10. 10
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Mining Multi-Level Association
Mining Multi-Dimensional Association
Mining Quantitative Association Rules
Mining Rare Patterns and Negative Patterns
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
11. 11
Mining Quantitative Associations
Techniques can be categorized by how numerical attributes,
such as age or salary are treated
1. Static discretization based on predefined concept
hierarchies (data cube methods)
2. Dynamic discretization based on data distribution
(quantitative rules, e.g., Agrawal & Srikant@SIGMOD96)
3. Clustering: Distance-based association (e.g., Yang &
Miller@SIGMOD97)
One dimensional clustering then association
4. Deviation: (such as Aumann and Lindell@KDD99)
Sex = female => Wage: mean=$7/hr (overall mean = $9)
12. 12
Static Discretization of Quantitative Attributes
Discretized prior to mining using concept hierarchy.
Numeric values are replaced by ranges
In relational database, finding all frequent k-predicate sets
will require k or k+1 table scans
Data cube is well suited for mining
The cells of an n-dimensional
cuboid correspond to the
predicate sets
Mining from data cubes
can be much faster
(income)
(age)
()
(buys)
(age, income) (age,buys) (income,buys)
(age,income,buys)
13. 13
Quantitative Association Rules Based on Statistical
Inference Theory [Aumann and Lindell@DMKD’03]
Finding extraordinary and therefore interesting phenomena, e.g.,
(Sex = female) => Wage: mean=$7/hr (overall mean = $9)
LHS: a subset of the population
RHS: an extraordinary behavior of this subset
The rule is accepted only if a statistical test (e.g., Z-test) confirms the
inference with high confidence
Subrule: highlights the extraordinary behavior of a subset of the pop.
of the super rule
E.g., (Sex = female) ^ (South = yes) => mean wage = $6.3/hr
Two forms of rules
Categorical => quantitative rules, or Quantitative => quantitative rules
E.g., Education in [14-18] (yrs) => mean wage = $11.64/hr
Open problem: Efficient methods for LHS containing two or more
quantitative attributes
14. 14
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Mining Multi-Level Association
Mining Multi-Dimensional Association
Mining Quantitative Association Rules
Mining Rare Patterns and Negative Patterns
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
15. 15
Negative and Rare Patterns
Rare patterns: Very low support but interesting
E.g., buying Rolex watches
Mining: Setting individual-based or special group-based
support threshold for valuable items
Negative patterns
Since it is unlikely that one buys Ford Expedition (an
SUV car) and Toyota Prius (a hybrid car) together, Ford
Expedition and Toyota Prius are likely negatively
correlated patterns
Negatively correlated patterns that are infrequent tend to
be more interesting than those that are frequent
16. 16
Defining Negative Correlated Patterns (I)
Definition 1 (support-based)
If itemsets X and Y are both frequent but rarely occur together, i.e.,
sup(X U Y) < sup (X) * sup(Y)
Then X and Y are negatively correlated
Problem: A store sold two needle 100 packages A and B, only one
transaction containing both A and B.
When there are in total 200 transactions, we have
s(A U B) = 0.005, s(A) * s(B) = 0.25, s(A U B) < s(A) * s(B)
When there are 105 transactions, we have
s(A U B) = 1/105, s(A) * s(B) = 1/103 * 1/103, s(A U B) > s(A) * s(B)
Where is the problem? —Null transactions, i.e., the support-based
definition is not null-invariant!
17. 17
Defining Negative Correlated Patterns (II)
Definition 2 (negative itemset-based)
X is a negative itemset if (1) X = Ā U B, where B is a set of positive
items, and Ā is a set of negative items, |Ā|≥ 1, and (2) s(X) ≥ μ
Itemsets X is negatively correlated, if
This definition suffers a similar null-invariant problem
Definition 3 (Kulzynski measure-based) If itemsets X and Y are
frequent, but (P(X|Y) + P(Y|X))/2 < є, where є is a negative pattern
threshold, then X and Y are negatively correlated.
Ex. For the same needle package problem, when no matter there are
200 or 105 transactions, if є = 0.01, we have
(P(A|B) + P(B|A))/2 = (0.01 + 0.01)/2 < є
18. 18
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
19. 19
Constraint-based (Query-Directed) Mining
Finding all the patterns in a database autonomously? — unrealistic!
The patterns could be too many but not focused!
Data mining should be an interactive process
User directs what to be mined using a data mining query
language (or a graphical user interface)
Constraint-based mining
User flexibility: provides constraints on what to be mined
Optimization: explores such constraints for efficient mining —
constraint-based mining: constraint-pushing, similar to push
selection first in DB query processing
Note: still find all the answers satisfying constraints, not finding
some answers in “heuristic search”
20. 20
Constraints in Data Mining
Knowledge type constraint:
classification, association, etc.
Data constraint — using SQL-like queries
find product pairs sold together in stores in Chicago this
year
Dimension/level constraint
in relevance to region, price, brand, customer category
Rule (or pattern) constraint
small sales (price < $10) triggers big sales (sum >
$200)
Interestingness constraint
strong rules: min_support 3%, min_confidence
60%
21. Meta-Rule Guided Mining
Meta-rule can be in the rule form with partially instantiated predicates
and constants
P1(X, Y) ^ P2(X, W) => buys(X, “iPad”)
The resulting rule derived can be
age(X, “15-25”) ^ profession(X, “student”) => buys(X, “iPad”)
In general, it can be in the form of
P1 ^ P2 ^ … ^ Pl => Q1 ^ Q2 ^ … ^ Qr
Method to find meta-rules
Find frequent (l+r) predicates (based on min-support threshold)
Push constants deeply when possible into the mining process (see
the remaining discussions on constraint-push techniques)
Use confidence, correlation, and other measures when possible
21
22. 22
Constraint-Based Frequent Pattern Mining
Pattern space pruning constraints
Anti-monotonic: If constraint c is violated, its further mining can
be terminated
Monotonic: If c is satisfied, no need to check c again
Succinct: c must be satisfied, so one can start with the data sets
satisfying c
Convertible: c is not monotonic nor anti-monotonic, but it can be
converted into it if items in the transaction can be properly
ordered
Data space pruning constraint
Data succinct: Data space can be pruned at the initial pattern
mining process
Data anti-monotonic: If a transaction t does not satisfy c, t can be
pruned from its further mining
23. 23
Pattern Space Pruning with Anti-Monotonicity Constraints
A constraint C is anti-monotone if the super
pattern satisfies C, all of its sub-patterns do so
too
In other words, anti-monotonicity: If an itemset
S violates the constraint, so does any of its
superset
Ex. 1. sum(S.price) v is anti-monotone
Ex. 2. range(S.profit) 15 is anti-monotone
Itemset ab violates C
So does every superset of ab
Ex. 3. sum(S.Price) v is not anti-monotone
Ex. 4. support count is anti-monotone: core
property used in Apriori
TID Transaction
10 a, b, c, d, f
20 b, c, d, f, g, h
30 a, c, d, e, f
40 c, e, f, g
TDB (min_sup=2)
Item Profit
a 40
b 0
c -20
d 10
e -30
f 30
g 20
h -10
24. 24
Pattern Space Pruning with Monotonicity Constraints
A constraint C is monotone if the pattern
satisfies C, we do not need to check C in
subsequent mining
Alternatively, monotonicity: If an itemset S
satisfies the constraint, so does any of its
superset
Ex. 1. sum(S.Price) v is monotone
Ex. 2. min(S.Price) v is monotone
Ex. 3. C: range(S.profit) 15
Itemset ab satisfies C
So does every superset of ab
TID Transaction
10 a, b, c, d, f
20 b, c, d, f, g, h
30 a, c, d, e, f
40 c, e, f, g
TDB (min_sup=2)
Item Profit
a 40
b 0
c -20
d 10
e -30
f 30
g 20
h -10
25. 25
Data Space Pruning with Data Anti-monotonicity
A constraint c is data anti-monotone if for a pattern
p cannot satisfy a transaction t under c, p’s
superset cannot satisfy t under c either
The key for data anti-monotone is recursive data
reduction
Ex. 1. sum(S.Price) v is data anti-monotone
Ex. 2. min(S.Price) v is data anti-monotone
Ex. 3. C: range(S.profit) 25 is data anti-
monotone
Itemset {b, c}’s projected DB:
T10’: {d, f, h}, T20’: {d, f, g, h}, T30’: {d, f, g}
since C cannot satisfy T10’, T10’ can be pruned
TID Transaction
10 a, b, c, d, f, h
20 b, c, d, f, g, h
30 b, c, d, f, g
40 c, e, f, g
TDB (min_sup=2)
Item Profit
a 40
b 0
c -20
d -15
e -30
f -10
g 20
h -5
26. 26
Pattern Space Pruning with Succinctness
Succinctness:
Given A1, the set of items satisfying a succinctness
constraint C, then any set S satisfying C is based on
A1 , i.e., S contains a subset belonging to A1
Idea: Without looking at the transaction database,
whether an itemset S satisfies constraint C can be
determined based on the selection of items
min(S.Price) v is succinct
sum(S.Price) v is not succinct
Optimization: If C is succinct, C is pre-counting pushable
29. 29
Constrained FP-Growth: Push a Succinct
Constraint Deep
Constraint:
min{S.price } <= 1
TID Items
100 1 3 4
200 2 3 5
300 1 2 3 5
400 2 5
TID Items
100 1 3
200 2 3 5
300 1 2 3 5
400 2 5
Remove
infrequent
length 1
FP-Tree
TID Items
100 3 4
300 2 3 5
1-Projected DB
No Need to project on 2, 3, or 5
30. 30
Constrained FP-Growth: Push a Data
Anti-monotonic Constraint Deep
Constraint:
min{S.price } <= 1
TID Items
100 1 3 4
200 2 3 5
300 1 2 3 5
400 2 5
TID Items
100 1 3
300 1 3
FP-Tree
Single branch, we are done
Remove from data
31. 31
Constrained FP-Growth: Push a
Data Anti-monotonic Constraint
Deep
Constraint:
range{S.price } > 25
min_sup >= 2
FP-Tree
TID Transaction
10 a, c, d, f, h
20 c, d, f, g, h
30 c, d, f, g
B-Projected DB
B
FP-Tree
TID Transaction
10 a, b, c, d, f, h
20 b, c, d, f, g, h
30 b, c, d, f, g
40 a, c, e, f, g
TID Transaction
10 a, b, c, d, f, h
20 b, c, d, f, g, h
30 b, c, d, f, g
40 a, c, e, f, g
Item Profit
a 40
b 0
c -20
d -15
e -30
f -10
g 20
h -5
Recursive
Data
Pruning
Single branch:
bcdfg: 2
32. 32
Convertible Constraints: Ordering Data in
Transactions
Convert tough constraints into anti-
monotone or monotone by properly
ordering items
Examine C: avg(S.profit) 25
Order items in value-descending
order
<a, f, g, d, b, h, c, e>
If an itemset afb violates C
So does afbh, afb*
It becomes anti-monotone!
TID Transaction
10 a, b, c, d, f
20 b, c, d, f, g, h
30 a, c, d, e, f
40 c, e, f, g
TDB (min_sup=2)
Item Profit
a 40
b 0
c -20
d 10
e -30
f 30
g 20
h -10
33. 33
Strongly Convertible Constraints
avg(X) 25 is convertible anti-monotone w.r.t.
item value descending order R: <a, f, g, d, b,
h, c, e>
If an itemset af violates a constraint C, so
does every itemset with af as prefix, such as
afd
avg(X) 25 is convertible monotone w.r.t. item
value ascending order R-1: <e, c, h, b, d, g, f,
a>
If an itemset d satisfies a constraint C, so
does itemsets df and dfa, which having d as
a prefix
Thus, avg(X) 25 is strongly convertible
Item Profit
a 40
b 0
c -20
d 10
e -30
f 30
g 20
h -10
34. 34
Can Apriori Handle Convertible Constraints?
A convertible, not monotone nor anti-monotone
nor succinct constraint cannot be pushed deep
into the an Apriori mining algorithm
Within the level wise framework, no direct
pruning based on the constraint can be made
Itemset df violates constraint C: avg(X) >=
25
Since adf satisfies C, Apriori needs df to
assemble adf, df cannot be pruned
But it can be pushed into frequent-pattern
growth framework!
Item Value
a 40
b 0
c -20
d 10
e -30
f 30
g 20
h -10
35. 35
Pattern Space Pruning w. Convertible Constraints
C: avg(X) >= 25, min_sup=2
List items in every transaction in value
descending order R: <a, f, g, d, b, h, c, e>
C is convertible anti-monotone w.r.t. R
Scan TDB once
remove infrequent items
Item h is dropped
Itemsets a and f are good, …
Projection-based mining
Imposing an appropriate order on item
projection
Many tough constraints can be converted into
(anti)-monotone
TID Transaction
10 a, f, d, b, c
20 f, g, d, b, c
30 a, f, d, c, e
40 f, g, h, c, e
TDB (min_sup=2)
Item Value
a 40
f 30
g 20
d 10
b 0
h -10
c -20
e -30
36. 36
Handling Multiple Constraints
Different constraints may require different or even
conflicting item-ordering
If there exists an order R s.t. both C1 and C2 are
convertible w.r.t. R, then there is no conflict between
the two convertible constraints
If there exists conflict on order of items
Try to satisfy one constraint first
Then using the order for the other constraint to
mine frequent itemsets in the corresponding
projected database
37. 37
What Constraints Are Convertible?
Constraint
Convertible anti-
monotone
Convertible
monotone
Strongly
convertible
avg(S) , v Yes Yes Yes
median(S) , v Yes Yes Yes
sum(S) v (items could be of any value,
v 0)
Yes No No
sum(S) v (items could be of any value,
v 0)
No Yes No
sum(S) v (items could be of any value,
v 0)
No Yes No
sum(S) v (items could be of any value,
v 0)
Yes No No
……
38. 38
Constraint-Based Mining — A General Picture
Constraint Anti-monotone Monotone Succinct
v S no yes yes
S V no yes yes
S V yes no yes
min(S) v no yes yes
min(S) v yes no yes
max(S) v yes no yes
max(S) v no yes yes
count(S) v yes no weakly
count(S) v no yes weakly
sum(S) v ( a S, a 0 ) yes no no
sum(S) v ( a S, a 0 ) no yes no
range(S) v yes no no
range(S) v no yes no
avg(S) v, { , , } convertible convertible no
support(S) yes no no
support(S) no yes no
39. 39
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
40. 40
Mining Colossal Frequent Patterns
F. Zhu, X. Yan, J. Han, P. S. Yu, and H. Cheng, “Mining Colossal
Frequent Patterns by Core Pattern Fusion”, ICDE'07.
We have many algorithms, but can we mine large (i.e., colossal)
patterns? ― such as just size around 50 to 100? Unfortunately, not!
Why not? ― the curse of “downward closure” of frequent patterns
The “downward closure” property
Any sub-pattern of a frequent pattern is frequent.
Example. If (a1, a2, …, a100) is frequent, then a1, a2, …, a100, (a1,
a2), (a1, a3), …, (a1, a100), (a1, a2, a3), … are all frequent! There
are about 2100 such frequent itemsets!
No matter using breadth-first search (e.g., Apriori) or depth-first
search (FPgrowth), we have to examine so many patterns
Thus the downward closure property leads to explosion!
41. 41
Closed/maximal patterns may
partially alleviate the problem but not
really solve it: We often need to mine
scattered large patterns!
Let the minimum support threshold
σ= 20
There are frequent patterns of
size 20
Each is closed and maximal
# patterns =
The size of the answer set is
exponential to n
Colossal Patterns: A Motivating Example
T1 = 1 2 3 4 ….. 39 40
T2 = 1 2 3 4 ….. 39 40
: .
: .
: .
: .
T40=1 2 3 4 ….. 39 40
20
40
T1 = 2 3 4 ….. 39 40
T2 = 1 3 4 ….. 39 40
: .
: .
: .
: .
T40=1 2 3 4 …… 39
n
n
n n
2
/
2
2
/
Then delete the items on the diagonal
Let’s make a set of 40 transactions
42. 42
Colossal Pattern Set: Small but Interesting
It is often the case that
only a small number of
patterns are colossal,
i.e., of large size
Colossal patterns are
usually attached with
greater importance than
those of small pattern
sizes
43. 43
Mining Colossal Patterns: Motivation and
Philosophy
Motivation: Many real-world tasks need mining colossal patterns
Micro-array analysis in bioinformatics (when support is low)
Biological sequence patterns
Biological/sociological/information graph pattern mining
No hope for completeness
If the mining of mid-sized patterns is explosive in size, there is no
hope to find colossal patterns efficiently by insisting “complete set”
mining philosophy
Jumping out of the swamp of the mid-sized results
What we may develop is a philosophy that may jump out of the
swamp of mid-sized results that are explosive in size and jump to
reach colossal patterns
Striving for mining almost complete colossal patterns
The key is to develop a mechanism that may quickly reach colossal
patterns and discover most of them
44. 44
Let the min-support threshold σ= 20
Then there are closed/maximal
frequent patterns of size 20
However, there is only one with size
greater than 20, (i.e., colossal):
α= {41,42,…,79} of size 39
Alas, A Show of Colossal Pattern Mining!
20
40
T1 = 2 3 4 ….. 39 40
T2 = 1 3 4 ….. 39 40
: .
: .
: .
: .
T40=1 2 3 4 …… 39
T41= 41 42 43 ….. 79
T42= 41 42 43 ….. 79
: .
: .
T60= 41 42 43 … 79
The existing fastest mining algorithms
(e.g., FPClose, LCM) fail to complete
running
Our algorithm outputs this colossal
pattern in seconds
45. 45
Methodology of Pattern-Fusion Strategy
Pattern-Fusion traverses the tree in a bounded-breadth way
Always pushes down a frontier of a bounded-size candidate
pool
Only a fixed number of patterns in the current candidate pool
will be used as the starting nodes to go down in the pattern tree
― thus avoids the exponential search space
Pattern-Fusion identifies “shortcuts” whenever possible
Pattern growth is not performed by single-item addition but by
leaps and bounded: agglomeration of multiple patterns in the
pool
These shortcuts will direct the search down the tree much more
rapidly towards the colossal patterns
46. 46
Observation: Colossal Patterns and Core Patterns
A colossal pattern α
D
Dα
α1
Transaction Database D
Dα1
Dα2
α2
α
αk
Dαk
Subpatterns α1 to αk cluster tightly around the colossal pattern α by
sharing a similar support. We call such subpatterns core patterns of α
47. 47
Robustness of Colossal Patterns
Core Patterns
Intuitively, for a frequent pattern α, a subpattern β is a τ-core
pattern of α if β shares a similar support set with α, i.e.,
where τ is called the core ratio
Robustness of Colossal Patterns
A colossal pattern is robust in the sense that it tends to have much
more core patterns than small patterns
|
|
|
|
D
D
1
0
48. 48
Example: Core Patterns
A colossal pattern has far more core patterns than a small-sized pattern
A colossal pattern has far more core descendants of a smaller size c
A random draw from a complete set of pattern of size c would more
likely to pick a core descendant of a colossal pattern
A colossal pattern can be generated by merging a set of core patterns
Transaction (# of Ts) Core Patterns (τ = 0.5)
(abe) (100) (abe), (ab), (be), (ae), (e)
(bcf) (100) (bcf), (bc), (bf)
(acf) (100) (acf), (ac), (af)
(abcef) (100) (ab), (ac), (af), (ae), (bc), (bf), (be) (ce), (fe), (e),
(abc), (abf), (abe), (ace), (acf), (afe), (bcf), (bce),
(bfe), (cfe), (abcf), (abce), (bcfe), (acfe), (abfe), (abcef)
49. 50
Colossal Patterns Correspond to Dense Balls
Due to their robustness,
colossal patterns correspond to
dense balls
Ω( 2^d) in population
A random draw in the pattern
space will hit somewhere in the
ball with high probability
50. 51
Idea of Pattern-Fusion Algorithm
Generate a complete set of frequent patterns up to a
small size
Randomly pick a pattern β, and β has a high probability to
be a core-descendant of some colossal pattern α
Identify all α’s descendants in this complete set, and
merge all of them ― This would generate a much larger
core-descendant of α
In the same fashion, we select K patterns. This set of
larger core-descendants will be the candidate pool for the
next iteration
51. 52
Pattern-Fusion: The Algorithm
Initialization (Initial pool): Use an existing algorithm to
mine all frequent patterns up to a small size, e.g., 3
Iteration (Iterative Pattern Fusion):
At each iteration, k seed patterns are randomly picked
from the current pattern pool
For each seed pattern thus picked, we find all the
patterns within a bounding ball centered at the seed
pattern
All these patterns found are fused together to generate
a set of super-patterns. All the super-patterns thus
generated form a new pool for the next iteration
Termination: when the current pool contains no more
than K patterns at the beginning of an iteration
52. 53
Why Is Pattern-Fusion Efficient?
A bounded-breadth pattern
tree traversal
It avoids explosion in
mining mid-sized ones
Randomness comes to help
to stay on the right path
Ability to identify “short-cuts”
and take “leaps”
fuse small patterns
together in one step to
generate new patterns of
significant sizes
Efficiency
53. 54
Pattern-Fusion Leads to Good Approximation
Gearing toward colossal patterns
The larger the pattern, the greater the chance it will
be generated
Catching outliers
The more distinct the pattern, the greater the chance
it will be generated
54. 55
Experimental Setting
Synthetic data set
Diagn an n x (n-1) table where ith row has integers from 1 to n
except i. Each row is taken as an itemset. min_support is n/2.
Real data set
Replace: A program trace data set collected from the “replace”
program, widely used in software engineering research
ALL: A popular gene expression data set, a clinical data on ALL-AML
leukemia (www.broad.mit.edu/tools/data.html).
Each item is a column, representing the activitiy level of
gene/protein in the same
Frequent pattern would reveal important correlation between
gene expression patterns and disease outcomes
55. 56
Experiment Results on Diagn
LCM run time increases
exponentially with pattern
size n
Pattern-Fusion finishes
efficiently
The approximation error of
Pattern-Fusion (with min-sup
20) in comparison with the
complete set) is rather close
to uniform sampling (which
randomly picks K patterns
from the complete answer
set)
56. 57
Experimental Results on ALL
ALL: A popular gene expression data set with 38
transactions, each with 866 columns
There are 1736 items in total
The table shows a high frequency threshold of 30
57. 58
Experimental Results on REPLACE
REPLACE
A program trace data set, recording 4395 calls
and transitions
The data set contains 4395 transactions with
57 items in total
With support threshold of 0.03, the largest
patterns are of size 44
They are all discovered by Pattern-Fusion with
different settings of K and τ, when started with
an initial pool of 20948 patterns of size <=3
58. 59
Experimental Results on REPLACE
Approximation error when
compared with the complete
mining result
Example. Out of the total 98
patterns of size >=42, when
K=100, Pattern-Fusion returns
80 of them
A good approximation to the
colossal patterns in the sense
that any pattern in the
complete set is on average at
most 0.17 items away from one
of these 80 patterns
59. 60
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
60. 61
Mining Compressed Patterns: δ-clustering
Why compressed patterns?
too many, but less meaningful
Pattern distance measure
δ-clustering: For each pattern P,
find all patterns which can be
expressed by P and their distance
to P are within δ (δ-cover)
All patterns in the cluster can be
represented by P
Xin et al., “Mining Compressed
Frequent-Pattern Sets”, VLDB’05
ID Item-Sets Support
P1 {38,16,18,12} 205227
P2 {38,16,18,12,17} 205211
P3 {39,38,16,18,12,17} 101758
P4 {39,16,18,12,17} 161563
P5 {39,16,18,12} 161576
Closed frequent pattern
Report P1, P2, P3, P4, P5
Emphasize too much on
support
no compression
Max-pattern, P3: info loss
A desirable output: P2, P3, P4
61. 62
Redundancy-Award Top-k Patterns
Why redundancy-aware top-k patterns?
Desired patterns: high
significance & low
redundancy
Propose the MMS
(Maximal Marginal
Significance) for
measuring the
combined significance
of a pattern set
Xin et al., Extracting
Redundancy-Aware
Top-K Patterns, KDD’06
62. 63
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
63. Do they all make sense?
What do they mean?
How are they useful?
diaper beer
female sterile (2) tekele
Annotate patterns with semantic information
morphological info. and simple statistics
Semantic Information
Not all frequent patterns are useful, only meaningful ones …
How to Understand and Interpret Patterns?
64. Word: “pattern” – from Merriam-Webster
A Dictionary Analogy
Non-semantic info.
Examples of Usage
Definitions indicating
semantics
Synonyms
Related Words
65. Semantic Analysis with Context Models
Task1: Model the context of a frequent pattern
Based on the Context Model…
Task2: Extract strongest context indicators
Task3: Extract representative transactions
Task4: Extract semantically similar patterns
67. 68
Chapter 7 : Advanced Frequent Pattern Mining
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Pattern Exploration and Application
Summary
68. 69
Summary
Roadmap: Many aspects & extensions on pattern mining
Mining patterns in multi-level, multi dimensional space
Mining rare and negative patterns
Constraint-based pattern mining
Specialized methods for mining high-dimensional data
and colossal patterns
Mining compressed or approximate patterns
Pattern exploration and understanding: Semantic
annotation of frequent patterns
69. 70
Ref: Mining Multi-Level and Quantitative Rules
Y. Aumann and Y. Lindell. A Statistical Theory for Quantitative Association
Rules, KDD'99
T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Data mining using
two-dimensional optimized association rules: Scheme, algorithms, and
visualization. SIGMOD'96.
J. Han and Y. Fu. Discovery of multiple-level association rules from large
databases. VLDB'95.
R.J. Miller and Y. Yang. Association rules over interval data. SIGMOD'97.
R. Srikant and R. Agrawal. Mining generalized association rules. VLDB'95.
R. Srikant and R. Agrawal. Mining quantitative association rules in large
relational tables. SIGMOD'96.
K. Wang, Y. He, and J. Han. Mining frequent itemsets using support
constraints. VLDB'00
K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Computing
optimized rectilinear regions for association rules. KDD'97.
70. 71
Ref: Mining Other Kinds of Rules
F. Korn, A. Labrinidis, Y. Kotidis, and C. Faloutsos. Ratio rules: A new
paradigm for fast, quantifiable data mining. VLDB'98
Y. Huhtala, J. Kärkkäinen, P. Porkka, H. Toivonen. Efficient Discovery of
Functional and Approximate Dependencies Using Partitions. ICDE’98.
H. V. Jagadish, J. Madar, and R. Ng. Semantic Compression and Pattern
Extraction with Fascicles. VLDB'99
B. Lent, A. Swami, and J. Widom. Clustering association rules. ICDE'97.
R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining
association rules. VLDB'96.
A. Savasere, E. Omiecinski, and S. Navathe. Mining for strong negative
associations in a large database of customer transactions. ICDE'98.
D. Tsur, J. D. Ullman, S. Abitboul, C. Clifton, R. Motwani, and S. Nestorov.
Query flocks: A generalization of association-rule mining. SIGMOD'98.
71. 72
Ref: Constraint-Based Pattern Mining
R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item
constraints. KDD'97
R. Ng, L.V.S. Lakshmanan, J. Han & A. Pang. Exploratory mining and pruning
optimizations of constrained association rules. SIGMOD’98
G. Grahne, L. Lakshmanan, and X. Wang. Efficient mining of constrained
correlated sets. ICDE'00
J. Pei, J. Han, and L. V. S. Lakshmanan. Mining Frequent Itemsets with
Convertible Constraints. ICDE'01
J. Pei, J. Han, and W. Wang, Mining Sequential Patterns with Constraints in
Large Databases, CIKM'02
F. Bonchi, F. Giannotti, A. Mazzanti, and D. Pedreschi. ExAnte: Anticipated
Data Reduction in Constrained Pattern Mining, PKDD'03
F. Zhu, X. Yan, J. Han, and P. S. Yu, “gPrune: A Constraint Pushing
Framework for Graph Pattern Mining”, PAKDD'07
72. 73
Ref: Mining Sequential Patterns
X. Ji, J. Bailey, and G. Dong. Mining minimal distinguishing subsequence patterns with
gap constraints. ICDM'05
H. Mannila, H Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event
sequences. DAMI:97.
J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan: Mining Sequential
Patterns Efficiently by Prefix-Projected Pattern Growth. ICDE'01.
R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and
performance improvements. EDBT’96.
X. Yan, J. Han, and R. Afshar. CloSpan: Mining Closed Sequential Patterns in Large
Datasets. SDM'03.
M. Zaki. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine
Learning:01.
73. Mining Graph and Structured Patterns
A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for
mining frequent substructures from graph data. PKDD'00
M. Kuramochi and G. Karypis. Frequent Subgraph Discovery. ICDM'01.
X. Yan and J. Han. gSpan: Graph-based substructure pattern mining.
ICDM'02
X. Yan and J. Han. CloseGraph: Mining Closed Frequent Graph Patterns.
KDD'03
X. Yan, P. S. Yu, and J. Han. Graph indexing based on discriminative frequent
structure analysis. ACM TODS, 30:960–993, 2005
X. Yan, F. Zhu, P. S. Yu, and J. Han. Feature-based substructure similarity
search. ACM Trans. Database Systems, 31:1418–1453, 2006
74
74. 75
Ref: Mining Spatial, Spatiotemporal, Multimedia Data
H. Cao, N. Mamoulis, and D. W. Cheung. Mining frequent spatiotemporal
sequential patterns. ICDM'05
D. Gunopulos and I. Tsoukatos. Efficient Mining of Spatiotemporal Patterns.
SSTD'01
K. Koperski and J. Han, Discovery of Spatial Association Rules in Geographic
Information Databases, SSD’95
H. Xiong, S. Shekhar, Y. Huang, V. Kumar, X. Ma, and J. S. Yoo. A framework
for discovering co-location patterns in data sets with extended spatial
objects. SDM'04
J. Yuan, Y. Wu, and M. Yang. Discovery of collocation patterns: From visual
words to visual phrases. CVPR'07
O. R. Zaiane, J. Han, and H. Zhu, Mining Recurrent Items in Multimedia with
Progressive Resolution Refinement. ICDE'00
75. 76
Ref: Mining Frequent Patterns in Time-Series Data
B. Ozden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. ICDE'98.
J. Han, G. Dong and Y. Yin, Efficient Mining of Partial Periodic Patterns in Time Series
Database, ICDE'99.
J. Shieh and E. Keogh. iSAX: Indexing and mining terabyte sized time series. KDD'08
B.-K. Yi, N. Sidiropoulos, T. Johnson, H. V. Jagadish, C. Faloutsos, and A. Biliris. Online
Data Mining for Co-Evolving Time Sequences. ICDE'00.
W. Wang, J. Yang, R. Muntz. TAR: Temporal Association Rules on Evolving Numerical
Attributes. ICDE’01.
J. Yang, W. Wang, P. S. Yu. Mining Asynchronous Periodic Patterns in Time Series Data.
TKDE’03
L. Ye and E. Keogh. Time series shapelets: A new primitive for data mining. KDD'09
76. 77
Ref: FP for Classification and Clustering
G. Dong and J. Li. Efficient mining of emerging patterns: Discovering
trends and differences. KDD'99.
B. Liu, W. Hsu, Y. Ma. Integrating Classification and Association Rule
Mining. KDD’98.
W. Li, J. Han, and J. Pei. CMAR: Accurate and Efficient Classification Based
on Multiple Class-Association Rules. ICDM'01.
H. Wang, W. Wang, J. Yang, and P.S. Yu. Clustering by pattern similarity in
large data sets. SIGMOD’ 02.
J. Yang and W. Wang. CLUSEQ: efficient and effective sequence
clustering. ICDE’03.
X. Yin and J. Han. CPAR: Classification based on Predictive Association
Rules. SDM'03.
H. Cheng, X. Yan, J. Han, and C.-W. Hsu, Discriminative Frequent Pattern
Analysis for Effective Classification”, ICDE'07
77. 78
Ref: Privacy-Preserving FP Mining
A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke. Privacy Preserving Mining
of Association Rules. KDD’02.
A. Evfimievski, J. Gehrke, and R. Srikant. Limiting Privacy Breaches in
Privacy Preserving Data Mining. PODS’03
J. Vaidya and C. Clifton. Privacy Preserving Association Rule Mining in
Vertically Partitioned Data. KDD’02
78. Mining Compressed Patterns
D. Xin, H. Cheng, X. Yan, and J. Han. Extracting redundancy-
aware top-k patterns. KDD'06
D. Xin, J. Han, X. Yan, and H. Cheng. Mining compressed
frequent-pattern sets. VLDB'05
X. Yan, H. Cheng, J. Han, and D. Xin. Summarizing itemset
patterns: A profile-based approach. KDD'05
79
79. Mining Colossal Patterns
F. Zhu, X. Yan, J. Han, P. S. Yu, and H. Cheng. Mining colossal
frequent patterns by core pattern fusion. ICDE'07
F. Zhu, Q. Qu, D. Lo, X. Yan, J. Han. P. S. Yu, Mining Top-K Large
Structural Patterns in a Massive Network. VLDB’11
80
80. 81
Ref: FP Mining from Data Streams
Y. Chen, G. Dong, J. Han, B. W. Wah, and J. Wang. Multi-Dimensional
Regression Analysis of Time-Series Data Streams. VLDB'02.
R. M. Karp, C. H. Papadimitriou, and S. Shenker. A simple algorithm for
finding frequent elements in streams and bags. TODS 2003.
G. Manku and R. Motwani. Approximate Frequency Counts over Data
Streams. VLDB’02.
A. Metwally, D. Agrawal, and A. El Abbadi. Efficient computation of
frequent and top-k elements in data streams. ICDT'05
81. 82
Ref: Freq. Pattern Mining Applications
T. Dasu, T. Johnson, S. Muthukrishnan, and V. Shkapenyuk. Mining Database Structure; or
How to Build a Data Quality Browser. SIGMOD'02
M. Khan, H. Le, H. Ahmadi, T. Abdelzaher, and J. Han. DustMiner: Troubleshooting
interactive complexity bugs in sensor networks., SenSys'08
Z. Li, S. Lu, S. Myagmar, and Y. Zhou. CP-Miner: A tool for finding copy-paste and related
bugs in operating system code. In Proc. 2004 Symp. Operating Systems Design and
Implementation (OSDI'04)
Z. Li and Y. Zhou. PR-Miner: Automatically extracting implicit programming rules and
detecting violations in large software code. FSE'05
D. Lo, H. Cheng, J. Han, S. Khoo, and C. Sun. Classification of software behaviors for failure
detection: A discriminative pattern mining approach. KDD'09
Q. Mei, D. Xin, H. Cheng, J. Han, and C. Zhai. Semantic annotation of frequent patterns.
ACM TKDD, 2007.
K. Wang, S. Zhou, J. Han. Profit Mining: From Patterns to Actions. EDBT’02.