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.
Chapter 4. Data Warehousing and On-Line Analytical Processing.pptSubrata 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
Data Mining: Concepts and Techniques — Chapter 2 —Salah Amean
the presentation contains the following :
-Data Objects and Attribute Types.
-Basic Statistical Descriptions of Data.
-Data Visualization.
-Measuring Data Similarity and Dissimilarity.
-Summary.
The document discusses implementing the Apriori algorithm for association rule mining using the Weka data mining tool. It describes Apriori as a classical bottom-up algorithm for mining frequent itemsets and relevant association rules from transactional databases. It also outlines how to create a sample dataset in Excel, convert it to ARFF format, load it into Weka, apply the Apriori algorithm to generate association rules, and interpret the results.
Teks tersebut membahas tentang linier programming, yang merupakan metode kuantitatif untuk pembuatan keputusan. Linier programming dikembangkan oleh George Dantzig untuk memecahkan masalah logistik militer selama Perang Dunia II. Teks tersebut juga menjelaskan sejarah, karakteristik, model, asumsi, dan bentuk-bentuk model linier programming.
The document discusses cross-validation, which is used to estimate how well a machine learning model will generalize to unseen data. It defines cross-validation as splitting a dataset into training and test sets to train a model on the training set and evaluate it on the held-out test set. Common types of cross-validation discussed are k-fold cross-validation, which repeats the process by splitting the data into k folds, and repeated holdout validation, which randomly samples subsets for training and testing over multiple repetitions.
Chapter 4. Data Warehousing and On-Line Analytical Processing.pptSubrata 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
Data Mining: Concepts and Techniques — Chapter 2 —Salah Amean
the presentation contains the following :
-Data Objects and Attribute Types.
-Basic Statistical Descriptions of Data.
-Data Visualization.
-Measuring Data Similarity and Dissimilarity.
-Summary.
The document discusses implementing the Apriori algorithm for association rule mining using the Weka data mining tool. It describes Apriori as a classical bottom-up algorithm for mining frequent itemsets and relevant association rules from transactional databases. It also outlines how to create a sample dataset in Excel, convert it to ARFF format, load it into Weka, apply the Apriori algorithm to generate association rules, and interpret the results.
Teks tersebut membahas tentang linier programming, yang merupakan metode kuantitatif untuk pembuatan keputusan. Linier programming dikembangkan oleh George Dantzig untuk memecahkan masalah logistik militer selama Perang Dunia II. Teks tersebut juga menjelaskan sejarah, karakteristik, model, asumsi, dan bentuk-bentuk model linier programming.
The document discusses cross-validation, which is used to estimate how well a machine learning model will generalize to unseen data. It defines cross-validation as splitting a dataset into training and test sets to train a model on the training set and evaluate it on the held-out test set. Common types of cross-validation discussed are k-fold cross-validation, which repeats the process by splitting the data into k folds, and repeated holdout validation, which randomly samples subsets for training and testing over multiple repetitions.
Lect6 Association rule & Apriori algorithmhktripathy
The document discusses the Apriori algorithm for mining association rules from transactional data. The Apriori algorithm uses a level-wise search where frequent itemsets are used to explore longer itemsets. It determines frequent itemsets by identifying individual frequent items and extending them to larger sets as long as they meet a minimum support threshold. The algorithm takes advantage of the fact that subsets of frequent itemsets must also be frequent to prune the search space. It performs candidate generation and pruning to efficiently identify all frequent itemsets in the transactional data.
ANALISIS REGRESI LINIER BERGANDA DAN PENGUJIAN ASUMSI RESIDUALArning Susilawati
ANALISIS REGRESI LINIER BERGANDA DAN PENGUJIAN ASUMSI RESIDUAL PADA DATA JUMLAH PERMINTAAN AIR BERSIH TERHADAP PENDAPATAN TOTAL KELUARGA, JUMLAH TANGGUNGAN KELUARGA, DAN PENGELUARAN ENERGI
Dokumen ini membahas konsep dan arsitektur data mining serta metode-metode yang digunakan dalam data mining seperti predictive modeling, clustering, association rule, dan sequence analysis beserta contoh-contoh penerapannya.
Dokumen tersebut menjelaskan tentang program integer yang merupakan bentuk lain dari program linier dimana asumsi divisibilitas melemah. Terdapat tiga jenis program integer yaitu integer murni, campuran, dan 0-1. Diberikan contoh soal program integer dan penyelesaiannya melalui pencabangan untuk mendapatkan solusi optimal berupa bilangan bulat.
Analisis regresi dan korelasi digunakan untuk mempelajari hubungan antara dua variabel atau lebih. Korelasi digunakan untuk mengukur kekuatan hubungan sementara regresi digunakan untuk memodelkan dan memprediksi hubungan tersebut. Metode kuadrat terkecil digunakan untuk menentukan model regresi terbaik berdasarkan minimisasi galat kuadrat.
This document provides an introduction to association rule mining. It begins with an overview of association rule mining and its application to market basket analysis. It then discusses key concepts like support, confidence and interestingness of rules. The document introduces the Apriori algorithm for mining association rules, which works in two steps: 1) generating frequent itemsets and 2) generating rules from frequent itemsets. It provides examples of how Apriori works and discusses challenges in association rule mining like multiple database scans and candidate generation.
This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.
Lect7 Association analysis to correlation analysishktripathy
Association rule mining aims to discover interesting relationships between items in large datasets. The document discusses key concepts in association rule mining including support, confidence, and correlation. Support measures how frequently an itemset occurs, while confidence measures the conditional probability of an itemset given another itemset. Correlation evaluates statistical dependence between itemsets and can be used to measure lift. Various measures are proposed to evaluate interestingness and redundancy of discovered rules.
This document outlines a chapter on data preprocessing that discusses data types, attributes, and preprocessing tasks. It begins by defining data and attributes, then describes different types of attributes like nominal, binary, ordinal, and numeric attributes. It also discusses different types of datasets like records, documents, transactions, and graphs. The major section on data preprocessing outlines why it is important and describes tasks like data cleaning, integration, transformation, reduction, and discretization to prepare dirty or unstructured data for analysis.
13 - 14 Regresi Linear Sederhana & Berganda.pdfElvi Rahmi
Regresi digunakan untuk memprediksi hubungan antara variabel-variabel berdasarkan data historis. Dokumen ini membahas regresi linear sederhana untuk memprediksi omzet penjualan berdasarkan pengalaman kerja, dan regresi linear berganda untuk memprediksi pengeluaran rumah tangga berdasarkan pendapatan dan jumlah anggota keluarga. Metode ini digunakan untuk peramalan dan pengambilan keputusan berdasarkan hubungan antara faktor-fak
Association rule mining finds frequent patterns and correlations among items in transaction databases. It involves two main steps:
1) Frequent itemset generation: Finds itemsets that occur together in a minimum number of transactions (above a support threshold). This is done efficiently using the Apriori algorithm.
2) Rule generation: Generates rules from frequent itemsets where the confidence (fraction of transactions with left hand side that also contain right hand side) is above a minimum threshold. Rules are a partitioning of an itemset into left and right sides.
Lambda-cut method converts a fuzzy set (or relation) into a crisp set (or relation) by defining membership values above a specified lambda value. It works by determining the crisp values where the membership is greater than or equal to lambda. The output of a fuzzy system can be a single fuzzy set or a union of multiple output fuzzy sets, depending on the number of rules.
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).
Lect6 Association rule & Apriori algorithmhktripathy
The document discusses the Apriori algorithm for mining association rules from transactional data. The Apriori algorithm uses a level-wise search where frequent itemsets are used to explore longer itemsets. It determines frequent itemsets by identifying individual frequent items and extending them to larger sets as long as they meet a minimum support threshold. The algorithm takes advantage of the fact that subsets of frequent itemsets must also be frequent to prune the search space. It performs candidate generation and pruning to efficiently identify all frequent itemsets in the transactional data.
ANALISIS REGRESI LINIER BERGANDA DAN PENGUJIAN ASUMSI RESIDUALArning Susilawati
ANALISIS REGRESI LINIER BERGANDA DAN PENGUJIAN ASUMSI RESIDUAL PADA DATA JUMLAH PERMINTAAN AIR BERSIH TERHADAP PENDAPATAN TOTAL KELUARGA, JUMLAH TANGGUNGAN KELUARGA, DAN PENGELUARAN ENERGI
Dokumen ini membahas konsep dan arsitektur data mining serta metode-metode yang digunakan dalam data mining seperti predictive modeling, clustering, association rule, dan sequence analysis beserta contoh-contoh penerapannya.
Dokumen tersebut menjelaskan tentang program integer yang merupakan bentuk lain dari program linier dimana asumsi divisibilitas melemah. Terdapat tiga jenis program integer yaitu integer murni, campuran, dan 0-1. Diberikan contoh soal program integer dan penyelesaiannya melalui pencabangan untuk mendapatkan solusi optimal berupa bilangan bulat.
Analisis regresi dan korelasi digunakan untuk mempelajari hubungan antara dua variabel atau lebih. Korelasi digunakan untuk mengukur kekuatan hubungan sementara regresi digunakan untuk memodelkan dan memprediksi hubungan tersebut. Metode kuadrat terkecil digunakan untuk menentukan model regresi terbaik berdasarkan minimisasi galat kuadrat.
This document provides an introduction to association rule mining. It begins with an overview of association rule mining and its application to market basket analysis. It then discusses key concepts like support, confidence and interestingness of rules. The document introduces the Apriori algorithm for mining association rules, which works in two steps: 1) generating frequent itemsets and 2) generating rules from frequent itemsets. It provides examples of how Apriori works and discusses challenges in association rule mining like multiple database scans and candidate generation.
This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.
Lect7 Association analysis to correlation analysishktripathy
Association rule mining aims to discover interesting relationships between items in large datasets. The document discusses key concepts in association rule mining including support, confidence, and correlation. Support measures how frequently an itemset occurs, while confidence measures the conditional probability of an itemset given another itemset. Correlation evaluates statistical dependence between itemsets and can be used to measure lift. Various measures are proposed to evaluate interestingness and redundancy of discovered rules.
This document outlines a chapter on data preprocessing that discusses data types, attributes, and preprocessing tasks. It begins by defining data and attributes, then describes different types of attributes like nominal, binary, ordinal, and numeric attributes. It also discusses different types of datasets like records, documents, transactions, and graphs. The major section on data preprocessing outlines why it is important and describes tasks like data cleaning, integration, transformation, reduction, and discretization to prepare dirty or unstructured data for analysis.
13 - 14 Regresi Linear Sederhana & Berganda.pdfElvi Rahmi
Regresi digunakan untuk memprediksi hubungan antara variabel-variabel berdasarkan data historis. Dokumen ini membahas regresi linear sederhana untuk memprediksi omzet penjualan berdasarkan pengalaman kerja, dan regresi linear berganda untuk memprediksi pengeluaran rumah tangga berdasarkan pendapatan dan jumlah anggota keluarga. Metode ini digunakan untuk peramalan dan pengambilan keputusan berdasarkan hubungan antara faktor-fak
Association rule mining finds frequent patterns and correlations among items in transaction databases. It involves two main steps:
1) Frequent itemset generation: Finds itemsets that occur together in a minimum number of transactions (above a support threshold). This is done efficiently using the Apriori algorithm.
2) Rule generation: Generates rules from frequent itemsets where the confidence (fraction of transactions with left hand side that also contain right hand side) is above a minimum threshold. Rules are a partitioning of an itemset into left and right sides.
Lambda-cut method converts a fuzzy set (or relation) into a crisp set (or relation) by defining membership values above a specified lambda value. It works by determining the crisp values where the membership is greater than or equal to lambda. The output of a fuzzy system can be a single fuzzy set or a union of multiple output fuzzy sets, depending on the number of rules.
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).
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.
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.
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
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 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.
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.
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 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 chapter discusses data mining trends and research frontiers. It covers mining complex data types like sequences, time series, graphs and networks. It also discusses other data mining methodologies like statistical data mining and visual data mining. The chapter describes various data mining applications in domains like finance, retail, science and engineering. It also discusses data mining applications for intrusion detection, recommender systems and the role of data mining in society. Emerging trends in data mining like mining multi-relational data and streaming data are also presented.
This chapter discusses outlier analysis and various methods for outlier detection. It defines outliers as data objects that differ significantly from normal data. Several types of outliers are described, including global outliers that differ from all other data, contextual outliers that differ based on selected context attributes, and collective outliers where a group of objects collectively differ. Statistical, proximity-based, and clustering-based methods are some common approaches for outlier detection discussed in the chapter. Statistical approaches assume data follows a stochastic model, while proximity-based methods use distance measures and density-based methods to identify outliers. Clustering-based methods identify outliers as objects not belonging to large, dense clusters of normal data. Both supervised and unsupervised learning techniques can be applied to outlier detection.
1. Clustering high-dimensional data presents unique challenges as traditional distance measures become less meaningful and clusters may only exist in subspaces of the data. 2. Subspace clustering methods aim to find clusters that exist in subspaces of the feature space rather than the entire space. 3. Popular subspace clustering methods include subspace search approaches that examine various subspaces, bi-clustering methods, and dimensionality reduction techniques.
This document summarizes chapter 10 of the book "Data Mining: Concepts and Techniques" which discusses cluster analysis. The chapter covers basic concepts of cluster analysis including partitioning, hierarchical, density-based and grid-based methods. It describes popular partitioning algorithms like k-means and k-medoids, and notes that k-means can be sensitive to outliers while k-medoids uses medioids which are less sensitive to outliers. The chapter also discusses evaluating clustering quality and major considerations for cluster analysis.
This chapter discusses advanced classification methods, including Bayesian belief networks, classification using backpropagation neural networks, support vector machines (SVM), and lazy learners. It describes Bayesian belief networks as probabilistic graphical models that represent conditional dependencies between variables. Backpropagation neural networks are introduced as a way to perform nonlinear regression to approximate functions through adjusting weights in a multi-layer feedforward network. SVM is covered as a method that transforms data into a higher dimensional space to find an optimal separating hyperplane, using support vectors.
This chapter discusses classification techniques for data mining, including decision trees, Bayes classification, and rule-based classification. It covers the basic process of classification, which involves constructing a model from training data and then using the model to classify new data. Decision tree induction and attribute selection measures like information gain, gain ratio, and Gini index are explained in detail. The chapter also discusses techniques for scaling up classification to large databases, addressing overfitting, and improving accuracy.
This chapter discusses techniques for computing data cubes from multidimensional datasets. It begins with basic concepts like data cube structure and computation. It then covers specific computation methods like multi-way array aggregation, bottom-up computation (BUC), and star-cubing. It also discusses challenges of computing high-dimensional cubes and approaches for minimizing computation. The chapter provides an overview of key data cube computation methods and optimization techniques.
This document discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data used for analysis and decision making. The key aspects of a data warehouse covered are its multidimensional data model using cubes and dimensions, extraction of data from multiple sources, and usage for querying, reporting, analytical processing, and data mining. Common data warehouse architectures and operations like star schemas, snowflake schemas, and OLAP functions such as roll-up and drill-down are also summarized.
This document discusses data preprocessing concepts from Chapter 3 of the book "Data Mining: Concepts and Techniques". It covers the major tasks in data preprocessing including data cleaning, integration, and reduction. Data cleaning involves handling incomplete, noisy, and inconsistent data through techniques like filling in missing values, identifying outliers, and resolving inconsistencies. Data integration combines data from multiple sources. Data reduction strategies aim to reduce the volume of data for analysis through dimensionality and numerosity reduction.
This document provides an overview of data mining concepts and techniques discussed in Chapter 2 of the textbook "Data Mining: Concepts and Techniques". It defines key terms like data objects, attributes, attribute types, statistical descriptions of data, and different methods of data visualization. Various techniques are described for understanding the characteristics of data sets through statistical measures, histograms, quantile plots, scatter plots and other approaches. Different styles of data visualization like pixel plots, geometric projections, icons and hierarchies are also summarized.
The document provides an overview of chapter 1 from the textbook "Data Mining: Concepts and Techniques". It discusses why data mining is necessary due to the massive growth of data, and defines data mining as the automated analysis of large data sets to extract useful patterns. It describes the evolution of database technology and sciences that led to the rise of data mining. It also gives a multi-dimensional view of data mining, covering the types of data that can be mined, patterns that can be discovered, technologies used, and applications.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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
nn
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
40T1 = 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
10
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
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