This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
A Fuzzy Algorithm for Mining High Utility Rare Itemsets – FHURIidescitation
Classical frequent itemset mining identifies frequent itemsets in transaction
databases using only frequency of item occurrences, without considering utility of items. In
many real world situations, utility of itemsets are based upon user’s perspective such as
cost, profit or revenue and are of significant importance. Utility mining considers using
utility factors in data mining tasks. Utility-based descriptive data mining aims at
discovering itemsets with high total utility is termed High Utility Itemset mining. High
Utility itemsets may contain frequent as well as rare itemsets. Classical utility mining only
considers items and their utilities as discrete values. In real world applications, such utilities
can be described by fuzzy sets. Thus itemset utility mining with fuzzy modeling allows item
utility values to be fuzzy and dynamic over time. In this paper, an algorithm, FHURI (Fuzzy
High Utility Rare Itemset Mining) is presented to efficiently and effectively mine very-high
(and high) utility rare itemsets from databases, by fuzzification of utility values. FHURI can
effectively extract fuzzy high utility rare itemsets by integrating fuzzy logic with high utility
rare itemset mining. FHURI algorithm may have practical meaning to real-world
marketing strategies. The results are shown using synthetic datasets.
Mining Frequent Closed Graphs on Evolving Data StreamsAlbert Bifet
Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this talk we present a framework for studying graph pattern mining on time-varying streams and large datasets.
MINING USER-AWARE RARE SEQUENTIAL TOPIC PATTERNS IN DOCUMENT STREAMSNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
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NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
New opportunities for connected data : Neo4j the graph databaseCédric Fauvet
Neo4j English presentation based on the French one.
Agenda :
The gaph theory
About Neo Technology
Uses cases
Vision of the market
The Neo4j Technology
Cypher the Neo4j’s « SQL »
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
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
Yemen is one of the countries with least measure in various fields. In this paper i talk about the role of ICT and Yemen unemployment.
here is the presentation slides of the paper.
http://www.slideshare.net/salahecom/ict-culturing-conference-presentation-presented-20131207
Contiki os timer is an essential topic in contiki OS. This presentation describes the different types of timers and their API .
It is following the same explanation as contiki OS wiki.
WSN protocol 802.15.4 together with cc2420 seminars Salah Amean
WSN protocol 802.15.4 together with cc2420 seminars . It is based on the standand of ieee802.15.4 and data sheet of the radio transceiver cc2420.
Note that some slides are borrowed.
ContikiMAC : Radio Duty Cycling ProtocolSalah Amean
Several MAC duty-cycle protocols have been proposed during the last decade to
address specific WSNs requirements and constraints such as a
low energy consumption linked to battery operated nodes
Radio Duty-Cycle (RDC) MAC protocols try to reduce the energy consumption by allowing a node to keep its radio-transceiver off most of the time.
This allow a node to avoid to keep the radio on unnecessarily, i.e when not involved in any transmission
Idle listening is used to solve such problem in which RDC MAC forces node to switch its transceiver between short active(listen) periods and long inactive (sleep) periods
. This presentation introduces a very important concept in wireless sensor network, particularly in handling transmission and reception of packets in very limited resources channel.
Location in ubiquitous computing, LOCATION SYSTEMSSalah Amean
This presentation is a simple effort to survey positioning systems which is part o
Introduction
Location system
Global Positioning System
Active Badge
Active Bat
Cricket
UbiSense
RADAR
Place Lab
PowerLine Positioning
ActiveFloor
Airbus and Tracking with Cameras
Credit:
1-the presentation follows the book of "Ubiquitous computing fundamentals by John Krumm " 2010 .
2- few videos are downloaded and integrated with the presentation. Most of the videos are important to explain about each topics they are placed in
Mobile apps-user interaction measurement & Apps ecosystemSalah Amean
This presentation talks about the user behaviours and the trend of mobile applications. It also talks about the behaviour of users downloading most common application on the market.
This report takes about the possibility of improving the employment rate in Yemen( since the amount of literacy is extremely low in Yemen in term of Internet).
So to solve the problem of unemployment we have to solve the severe problem of literacy in term of ICT or literacy in general.
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.
Characterizing wi fi-link_in_open_outdoor_netwoSalah Amean
long distance wifi is really an important concept to deliver internet to remote places in developed countries as well as the poor and developing countries.
This presentation is great introduction to the dynamic host configuraton protocol "DHCP".
It also provides more protocol based details together with the comparison to BOOTP protocol.
Contiki is an open source operating system for the Internet of Things. Contiki connects tiny low-cost, low-power microcontrollers to the Internet.
the presentation explains how to install the simulator, teach the reader some concepts of contiki OS, goes through API used in platform specific examples, and most importantly explains some example(Blinking example, Light and temperature sensor web demo).
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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
Sequential Pattern Mining
Graph Pattern Mining
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%]
reduced support
Skim Milk
[support = 4%]
Level 1
min_sup = 5%
Level 2
min_sup = 5%
Level 1
min_sup = 5%
Level 2
min_sup = 3%
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
Sequential Pattern Mining
Graph Pattern Mining
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
Sequential Pattern Mining
Graph Pattern Mining
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
()
(age) (income)
(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
Sequential Pattern Mining
Graph Pattern Mining
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 = 0.05, 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
Sequential Pattern Mining
Graph Pattern Mining
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. Pattern Space Pruning with Anti-Monotonicity Constraints
23
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
TDB (min_sup=2)
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
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
TDB (min_sup=2)
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
Item Profit
a 40
b 0
c -20
d 10
e -30
f 30
g 20
h -10
25. Data Space Pruning with Data Anti-monotonicity
25
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
TDB (min_sup=2)
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
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
1-Projected DB
TID Items
100 3 4
300 2 3 5
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
Remove from data
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
31. 31
Constrained FP-Growth: Push a
Data Anti-monotonic Constraint
Deep
Constraint:
range{S.price } > 25
min_sup >= 2
FP-Tree
B-Projected DB
TID Transaction
10 a, c, d, f, h
20 c, d, f, g, h
30 c, d, f, g
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!
TDB (min_sup=2)
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
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. Pattern Space Pruning w. Convertible Constraints
Item Value
a 40
f 30
g 20
d 10
b 0
h -10
c -20
e -30
TDB (min_sup=2)
35
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
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
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
38. 38
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
……
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
Sequential Pattern Mining
Graph Pattern Mining
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. Colossal Patterns: A Motivating Example
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 =
41
20
n n 2
2/
The size of the answer set is
exponential to n
Let’s make a set of 40 transactions
T1 = 1 2 3 4 ….. 39 40
T2 = 1 2 3 4 ….. 39 40
: .
: .
: .
: .
T40=1 2 3 4 ….. 39 40
40
T1 = 2 3 4 ….. 39 40
T2 = 1 3 4 ….. 39 40
: .
: .
: .
: .
T40=1 2 3 4 …… 39
n / 2
n
Then delete the items on the diagonal
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
Alas, A Show of Colossal Pattern Mining!
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):
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
20
α= {41,42,…,79} of size 39
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
α
α1
Transaction Database D
Dα1
Dα2
α2
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.,
D
| |
D
| |
1 0
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
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. Pattern-Fusion Leads to Good Approximation
54
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
Sequential Pattern Mining
Graph Pattern Mining
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
Sequential Pattern Mining
Graph Pattern Mining
Summary
63. Sequence Databases & Sequential Patterns
Transaction databases, time-series databases vs. sequence
databases
Frequent patterns vs. (frequent) sequential patterns
Applications of sequential pattern mining
Customer shopping sequences:
First buy computer, then CD-ROM, and then digital
camera, within 3 months.
Medical treatments, natural disasters (e.g., earthquakes),
science & eng. processes, stocks and markets, etc.
Telephone calling patterns, Weblog click streams
Program execution sequence data sets
DNA sequences and gene structures
64. What Is Sequential Pattern Mining?
Given a set of sequences, find the complete set of
frequent subsequences
A sequence database
A sequence : < (ef) (ab) (df) c b >
• An element may contain a set of items
• Items within an element are unordered
and we list them alphabetically
<a(bc)dc> is a subsequence
of <a(abc)(ac)d(cf)>
SID sequence
10 <a(abc)(ac)d(cf)>
20 <(ad)c(bc)(ae)>
30 <(ef)(ab)(df)cb>
40 <eg(af)cbc>
Given support threshold min_sup = 2, <(ab)c> is a
sequential pattern
Sequential pattern mining: find the complete set of patterns,
satisfying the minimum support (frequency) threshold
65. Sequential Pattern Mining Algorithms
Concept introduction and an initial Apriori-like algorithm
Agrawal & Srikant: Mining sequential patterns, ICDE’95
Requirement: efficient, scalable, complete, minimal database scans,
and be able to incorporate various kinds of user-specific constraints
Representative algorithms
GSP (Generalized Sequential Patterns): Srikant & Agrawal @
EDBT’96)
Vertical format-based mining: SPADE (Zaki@Machine Leanining’00)
Pattern-growth methods: PrefixSpan (Pei, Han et al. @ICDE’01)
Constraint-based sequential pattern mining (SPIRIT: Garofalakis,
Rastogi, Shim@VLDB’99; Pei, Han, Wang @ CIKM’02)
Mining closed sequential patterns: CloSpan (Yan, Han et al. @SDM’03)
66. The Apriori Property of Sequential Patterns
A basic property: Apriori (Agrawal & Sirkant’94)
If a sequence S is not frequent
Then none of the super-sequences of S is frequent
E.g, <hb> is infrequent so do <hab> and <(ah)b>
Seq. ID Sequence Given support threshold
10 <(bd)cb(ac)>
20 <(bf)(ce)b(fg)>
30 <(ah)(bf)abf>
40 <(be)(ce)d>
50 <a(bd)bcb(ade)>
min_sup =2
67. GSP—Generalized Sequential Pattern Mining
GSP (Generalized Sequential Pattern) mining algorithm
proposed by Agrawal and Srikant, EDBT’96
Outline of the method
Initially, every item in DB is a candidate of length-1
for each level (i.e., sequences of length-k) do
scan database to collect support count for each
candidate sequence
generate candidate length-(k+1) sequences from
length-k frequent sequences using Apriori
repeat until no frequent sequence or no candidate can
be found
Major strength: Candidate pruning by Apriori
68. Finding Length-1 Sequential Patterns
Examine GSP using an example
Initial candidates: all singleton sequences
<a>, <b>, <c>, <d>, <e>, <f>, <g>, <h>
Scan database once, count support for candidates
min_sup =2
Seq. ID Sequence
10 <(bd)cb(ac)>
20 <(bf)(ce)b(fg)>
30 <(ah)(bf)abf>
40 <(be)(ce)d>
50 <a(bd)bcb(ade)>
Cand Sup
<a> 3
<b> 5
<c> 4
<d> 3
<e> 3
<f> 2
<g> 1
<h> 1
70. The GSP Mining Process
<(bd)cba>
<abba> <(bd)bc> …
<abb> <aab> <aba> <baa> <bab> …
<aa> <ab> … <af> <ba> <bb> … <ff> <(ab)> … <(ef)>
<a> <b> <c> <d> <e> <f> <g> <h>
5th scan: 1 cand. 1 length-5 seq.
pat.
4th scan: 8 cand. 6 length-4 seq.
pat.
3rd scan: 46 cand. 19 length-3 seq.
pat. 20 cand. not in DB at all
2nd scan: 51 cand. 19 length-2 seq.
pat. 10 cand. not in DB at all
1st scan: 8 cand. 6 length-1 seq.
pat.
Cand. cannot pass
sup. threshold
Cand. not in DB at all
Seq. ID Sequence
10 <(bd)cb(ac)>
20 <(bf)(ce)b(fg)>
30 <(ah)(bf)abf>
40 <(be)(ce)d>
50 <a(bd)bcb(ade)>
min_sup =2
71. The SPADE Algorithm
SPADE (Sequential PAttern Discovery using Equivalent
Class) developed by Zaki 2001
A vertical format sequential pattern mining method
A sequence database is mapped to a large set of
Item: <SID, EID>
Sequential pattern mining is performed by
growing the subsequences (patterns) one item at a
time by Apriori candidate generation
73. Bottlenecks of GSP and SPADE
A huge set of candidates could be generated
1,000 frequent length-1 sequences generate s huge number of
length-2 candidates!
1000 1000
Multiple scans of database in mining
Breadth-first search
1000 999
Mining long sequential patterns by growing from shorter patterns
Needs an exponential number of short candidates
A length-100 sequential pattern needs 1030
candidate sequences!
1,499,500
2
100 30
100
1
2 1 10
100
i i
74. PrefixSpan: Mining Sequential Patterns by
Prefix Projections
PrefixSpan Mining framework
Prefix Suffix (Prefix-Based Projection)
<a> <(abc)(ac)d(cf)>
<aa> <(_bc)(ac)d(cf)>
<ab> <(_c)(ac)d(cf)>
Step 1: find length-1 sequential patterns
<a>, <b>, <c>, <d>, <e>, <f>
Step 2: divide search space. The complete set of seq. pat. can
be partitioned into 6 subsets:
The ones having prefix <a>;
The ones having prefix <b>;
…
The ones having prefix <f>
SID sequence
10 <a(abc)(ac)d(cf)>
20 <(ad)c(bc)(ae)>
30 <(ef)(ab)(df)cb>
40 <eg(af)cbc>
Prefix and suffix
Given sequence <a(abc)(ac)d(cf)>
Prefixes: <a>, <aa>, <a(ab)>
and <a(abc)>
75. Finding Seq. Patterns with Prefix <a>
Only need to consider projections w.r.t. <a>
<a>-projected database:
<(abc)(ac)d(cf)>
<(_d)c(bc)(ae)>
<(_b)(df)cb>
<(_f)cbc>
Find all the length-2 seq. pat. Having prefix <a>: <aa>,
<ab>, <(ab)>, <ac>, <ad>, <af>
Further partition into 6 subsets
Having prefix <aa>;
…
Having prefix <af>
SID sequence
10 <a(abc)(ac)d(cf)>
20 <(ad)c(bc)(ae)>
30 <(ef)(ab)(df)cb>
40 <eg(af)cbc>
76. Completeness of PrefixSpan
SDB
SID sequence
10 <a(abc)(ac)d(cf)>
20 <(ad)c(bc)(ae)>
30 <(ef)(ab)(df)cb>
40 <eg(af)cbc>
Length-1 sequential patterns
<a>, <b>, <c>, <d>, <e>, <f>
<a>-projected database
<(abc)(ac)d(cf)>
<(_d)c(bc)(ae)>
<(_b)(df)cb>
<(_f)cbc>
Having prefix <b>
Length-2 sequential
patterns
<aa>, <ab>, <(ab)>,
<ac>, <ad>, <af>
Having prefix <a>
Having prefix <aa>
Having prefix <af>
<aa>-proj. db … <af>-proj. db
Having prefix <c>, …, <f>
<b>-projected database …
… …
Major strength of PrefixSpan:
• No candidate sequence needs to be generated
• Projected databases keep shrinking
77. Speed-up by Pseudo-Projection
Major cost of PrefixSpan: Constructing projected databases
Postfixes of sequences often appear repeatedly in
recursive projected databases
When (projected) database can be held in main memory,
use pointers to form pseudo-projections
Pointer to the sequence
Offset of the postfix
s=<a(abc)(ac)d(cf)>
<a>
<(abc)(ac)d(cf)>
<ab>
<(_c)(ac)d(cf)>
s|<a>: ( , 2)
s|<ab>: ( , 4)
78. Pseudo-Projection vs. Physical Projection
Pseudo-projection avoids physically copying postfixes
Efficient in running time and space when database
can be held in main memory
However, it is not efficient when database cannot fit in
main memory
Disk-based random accessing is very costly
Suggested Approach:
Integration of physical and pseudo-projection
Swapping to pseudo-projection when the data set fits
in memory
79. Performance of Sequential Pattern Mining
Algorithms
Performance comparison
on Gazelle data set
Performance comparison:
with pseudo-projection vs.
without pseudo-projection
Performance comparison
on data set C10T8S8I8
80. CloSpan: Mining Closed Sequential
Patterns
A closed sequential pattern s: there
exists no superpattern s’ such that s’ כ
s, and s’ and s have the same support
Which one is closed? <abc>: 20,
<abcd>:20, <abcde>: 15
Why mine close seq. patterns?
Reduces the number of (redundant)
patterns but attains the same
expressive power
Property: If s’ כ s, closed iff two
project DBs have the same size
Using Backward Subpattern and
Backward Superpattern pruning to
prune redundant search space
82. Constraint-Based Seq.-Pattern Mining
Constraint-based sequential pattern mining
Constraints: User-specified, for focused mining of desired
patterns
How to explore efficient mining with constraints? —
Optimization
Classification of constraints
Anti-monotone: E.g., value_sum(S) < 150, min(S) > 10
Monotone: E.g., count (S) > 5, S {PC, digital_camera}
Succinct: E.g., length(S) 10, S {Pentium, MS/Office,
MS/Money}
Convertible: E.g., value_avg(S) < 25, profit_sum (S) >
160, max(S)/avg(S) < 2, median(S) – min(S) > 5
Inconvertible: E.g., avg(S) – median(S) = 0
83. From Sequential Patterns to Structured Patterns
Sets, sequences, trees, graphs, and other structures
Transaction DB: Sets of items
{{i1, i2, …, im}, …}
Seq. DB: Sequences of sets:
{<{i1, i2}, …, {im, in, ik}>, …}
Sets of Sequences:
{{<i1, i2>, …, <im, in, ik>}, …}
Sets of trees: {t1, t2, …, tn}
Sets of graphs (mining for frequent subgraphs):
{g1, g2, …, gn}
Mining structured patterns in XML documents, bio-chemical
structures, etc.
84. Alternative to Sequential Patterns: Episodes
and Episode Pattern Mining
Alternative patterns: Episodes and regular expressions
Serial episodes: A B
Parallel episodes: A & B
Regular expressions: (A|B)C*(D E)
Methods for episode pattern mining
Method 1: Variations of Apriori/GSP-like algorithms
Method 2: Projection-based pattern growth
Can you work out the details?
Question: What is the difference between mining
episodes and constraint-based pattern mining?
85. 86
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
Sequential Pattern Mining
Graph Pattern Mining
Summary
86. Graph Pattern Mining
Frequent subgraphs
A (sub)graph is frequent if its support (occurrence
frequency) in a given dataset is no less than a
minimum support threshold
Applications of graph pattern mining
Mining biochemical structures
Program control flow analysis
Mining XML structures or Web communities
Building blocks for graph classification, clustering,
compression, comparison, and correlation analysis
88. 89
Properties of Graph Mining Algorithms
Search order
breadth vs. depth
Generation of candidate subgraphs
apriori vs. pattern growth
Elimination of duplicate subgraphs
passive vs. active
Support calculation
embedding store or not
Discover order of patterns
path tree graph
90. 91
Apriori-Based, Breadth-First Search
Methodology: breadth-search, joining two graphs
AGM (Inokuchi, et al. PKDD’00)
generates new graphs with one more node
FSG (Kuramochi and Karypis ICDM’01)
generates new graphs with one more edge
91. 92
Pattern Growth Method
…
G
G1
G2
Gn
k-edge
(k+1)-edge
(k+2)-edge
…
…
duplicate
graph
92. 93
GSPAN (Yan and Han ICDM’02)
Right-Most Extension
Theorem: Completeness
The Enumeration of Graphs
using Right-most Extension is
COMPLETE
93. 94
DFS Code
Flatten a graph into a sequence using depth first
search
0
1
2
3
4
e0: (0,1)
e1: (1,2)
e2: (2,0)
e3: (2,3)
e4: (3,1)
e5: (2,4)
94. 95
DFS Lexicographic Order
Let Z be the set of DFS codes of all graphs. Two DFS
codes a and b have the relation a<=b (DFS
Lexicographic Order in Z) if and only if one of the
following conditions is true. Let
a = (x0, x1, …, xn) and
b = (y0, y1, …, yn),
(i) if there exists t, 0<= t <= min(m,n), xk=yk for all
k, s.t. k<t, and xt < yt
(ii) xk=yk for all k, s.t. 0<= k<= m and m <= n.
95. 96
DFS Code Extension
Let a be the minimum DFS code of a graph G and b be
a non-minimum DFS code of G. For any DFS code d
generated from b by one right-most extension,
(i) d is not a minimum DFS code,
(ii) min_dfs(d) cannot be extended from b, and
(iii) min_dfs(d) is either less than a or can be
extended from a.
THEOREM [ RIGHT-EXTENSION ]
The DFS code of a graph extended from a
Non-minimum DFS code is NOT MINIMUM
96. 97
GASTON (Nijssen and Kok KDD’04)
Extend graphs directly
Store embeddings
Separate the discovery of different types of
graphs
path tree graph
Simple structures are easier to mine and
duplication detection is much simpler
97. 98
Graph Pattern Explosion Problem
If a graph is frequent, all of its subgraphs are
frequent ─ the Apriori property
An n-edge frequent graph may have 2n
subgraphs
Among 422 chemical compounds which are
confirmed to be active in an AIDS antiviral
screen dataset, there are 1,000,000 frequent
graph patterns if the minimum support is 5%
98. 99
Closed Frequent Graphs
Motivation: Handling graph pattern explosion
problem
Closed frequent graph
A frequent graph G is closed if there exists no
supergraph of G that carries the same support
as G
If some of G’s subgraphs have the same support,
it is unnecessary to output these subgraphs
(nonclosed graphs)
Lossless compression: still ensures that the
mining result is complete
99. 100
CLOSEGRAPH (Yan & Han, KDD’03)
A Pattern-Growth Approach
…
G
G1
G2
Gn
k-edge
(k+1)-edge
At what condition, can we
stop searching their children
i.e., early termination?
If G and G’ are frequent, G is a
subgraph of G’. If in any part
of the graph in the dataset
where G occurs, G’ also
occurs, then we need not grow
G, since none of G’s children will
be closed except those of G’.
100. 101
Handling Tricky Exception Cases
a
c
b
d
(graph 1)
(pattern 1)
(pattern 2)
a
c
b
d
(graph 2)
a b
a
c
d
101. 102
Experimental Result
The AIDS antiviral screen compound dataset
from NCI/NIH
The dataset contains 43,905 chemical
compounds
Among these 43,905 compounds, 423 of them
belongs to CA, 1081 are of CM, and the
remaining are in class CI
105. Performance Comparison: Frequent vs. Closed
FSG
Gspan
CloseGraph
106
# of Patterns: Frequent vs. Closed
CA
1.0E+06
1.0E+05
1.0E+04
1.0E+03
1.0E+02
frequent graphs
closed frequent graphs
0.05 0.06 0.07 0.08 0.1
Minimum support
Number of patterns
10000
1000
100
10
1
0.05 0.06 0.07 0.08 0.1
Minimum support
Run time (sec)
Runtime: Frequent vs. Closed
CA
106. 107
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
107. 108
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
109. 110
Ref: Mining Multi-Level and Quantitative Rules
R. Srikant and R. Agrawal. Mining generalized association rules.
VLDB'95.
J. Han and Y. Fu. Discovery of multiple-level association rules from
large databases. VLDB'95.
R. Srikant and R. Agrawal. Mining quantitative association rules in
large relational tables. SIGMOD'96.
T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Data mining
using two-dimensional optimized association rules: Scheme,
algorithms, and visualization. SIGMOD'96.
K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama.
Computing optimized rectilinear regions for association rules. KDD'97.
R.J. Miller and Y. Yang. Association rules over interval data.
SIGMOD'97.
Y. Aumann and Y. Lindell. A Statistical Theory for Quantitative
Association Rules KDD'99.
110. 111
Ref: Mining Other Kinds of Rules
R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining
association rules. VLDB'96.
B. Lent, A. Swami, and J. Widom. Clustering association rules.
ICDE'97.
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.
F. Korn, A. Labrinidis, Y. Kotidis, and C. Faloutsos. Ratio rules: A new
paradigm for fast, quantifiable data mining. VLDB'98.
F. Zhu, X. Yan, J. Han, P. S. Yu, and H. Cheng, “Mining Colossal
Frequent Patterns by Core Pattern Fusion”, ICDE'07.
111. 112
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
112. 113
Ref: Mining Sequential and Structured Patterns
R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations
and performance improvements. EDBT’96.
H. Mannila, H Toivonen, and A. I. Verkamo. Discovery of frequent
episodes in event sequences. DAMI:97.
M. Zaki. SPADE: An Efficient Algorithm for Mining Frequent Sequences.
Machine Learning:01.
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.
M. Kuramochi and G. Karypis. Frequent Subgraph Discovery. ICDM'01.
X. Yan, J. Han, and R. Afshar. CloSpan: Mining Closed Sequential
Patterns in Large Datasets. SDM'03.
X. Yan and J. Han. CloseGraph: Mining Closed Frequent Graph
Patterns. KDD'03.
113. 114
Ref: Mining Spatial, Multimedia, and Web Data
K. Koperski and J. Han, Discovery of Spatial Association Rules in
Geographic Information Databases, SSD’95.
O. R. Zaiane, M. Xin, J. Han, Discovering Web Access Patterns and
Trends by Applying OLAP and Data Mining Technology on Web Logs.
ADL'98.
O. R. Zaiane, J. Han, and H. Zhu, Mining Recurrent Items in
Multimedia with Progressive Resolution Refinement. ICDE'00.
D. Gunopulos and I. Tsoukatos. Efficient Mining of Spatiotemporal
Patterns. SSTD'01.
114. 115
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.
H. Lu, L. Feng, and J. Han. Beyond Intra-Transaction Association
Analysis: Mining Multi-Dimensional Inter-Transaction Association Rules.
TOIS:00.
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.
115. 116
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.
116. 117
Ref: Stream and Privacy-Preserving FP Mining
A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke. Privacy Preserving
Mining of Association Rules. KDD’02.
J. Vaidya and C. Clifton. Privacy Preserving Association Rule Mining
in Vertically Partitioned Data. KDD’02.
G. Manku and R. Motwani. Approximate Frequency Counts over
Data Streams. VLDB’02.
Y. Chen, G. Dong, J. Han, B. W. Wah, and J. Wang. Multi-
Dimensional Regression Analysis of Time-Series Data Streams.
VLDB'02.
C. Giannella, J. Han, J. Pei, X. Yan and P. S. Yu. Mining Frequent
Patterns in Data Streams at Multiple Time Granularities, Next
Generation Data Mining:03.
A. Evfimievski, J. Gehrke, and R. Srikant. Limiting Privacy Breaches
in Privacy Preserving Data Mining. PODS’03.
117. 118
Ref: Other Freq. Pattern Mining Applications
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.
T. Dasu, T. Johnson, S. Muthukrishnan, and V. Shkapenyuk.
Mining Database Structure; or How to Build a Data Quality
Browser. SIGMOD'02.
K. Wang, S. Zhou, J. Han. Profit Mining: From Patterns to Actions.
EDBT’02.
118. Ref: Mining Sequential Patterns
R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance
improvements. EDBT’96.
H. Mannila, H Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event
sequences. DAMI:97.
M. Zaki. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning,
2001.
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 (TKDE’04).
J. Pei, J. Han and W. Wang, Constraint-Based Sequential Pattern Mining in Large
Databases, CIKM'02.
X. Yan, J. Han, and R. Afshar. CloSpan: Mining Closed Sequential Patterns in Large
Datasets. SDM'03.
J. Wang and J. Han, BIDE: Efficient Mining of Frequent Closed Sequences, ICDE'04.
H. Cheng, X. Yan, and J. Han, IncSpan: Incremental Mining of Sequential Patterns in
Large Database, KDD'04.
J. Han, G. Dong and Y. Yin, Efficient Mining of Partial Periodic Patterns in Time Series
Database, ICDE'99.
J. Yang, W. Wang, and P. S. Yu, Mining asynchronous periodic patterns in time series
data, KDD'00.
120. 121
Chapter 7 : Advanced Frequent Pattern Mining
Frequent Pattern and Association Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Mining Multilevel Association
Mining Multi-Dimensional Association
Mining Quantitative Association Rules
Exploring Alternative Approaches to Improve Efficiency and Scalability
Mining Closed and Max Patterns
Scalable Pattern Mining in High-Dimensional Data
Mining Colossal Patterns
Mining Beyond Typical Frequent Patterns
Mining Infrequent and Negative Patterns
Mining Compressed and Approximate Patterns
Constraint-Based Frequent Pattern Mining
Metarule-Guided Mining of Association Rules
Constraint-Based Pattern Generation: Montonicity, Anti-monotonicity,
Succinctness, and Data Anti-monotonicity
Convertible Constraints: Ordering Data in Transactions
Advanced Applications of Frequent Patterns
Towards pattern-based classification and cluster analysis
Context Analysis: Generating Semantic Annotations for Frequent Patterns
Summary
121. 122
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
122. How to Understand and Interpret Patterns?
Do they all make sense?
What do they mean?
How are they useful?
diaper beer
female sterile (2) tekele
morphological info. and simple statistics
Semantic Information
Not all frequent patterns are useful, only meaningful ones …
Annotate patterns with semantic information
123. A Dictionary Analogy
Word: “pattern” – from Merriam-Webster
Non-semantic info.
Definitions indicating
semantics
Examples of Usage
Synonyms
Related Words
124. 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
126. Frequent Subgraph Mining Approaches
Apriori-based approach
AGM/AcGM: Inokuchi, et al. (PKDD’00)
FSG: Kuramochi and Karypis (ICDM’01)
PATH#: Vanetik and Gudes (ICDM’02, ICDM’04)
FFSM: Huan, et al. (ICDM’03)
Pattern growth approach
MoFa, Borgelt and Berthold (ICDM’02)
gSpan: Yan and Han (ICDM’02)
Gaston: Nijssen and Kok (KDD’04)
127. 128
PATH (Vanetik and Gudes ICDM’02, ’04)
Apriori-based approach
Building blocks: edge-disjoint path
A graph with 3 edge-disjoint
paths
• construct frequent paths
• construct frequent graphs with
2 edge-disjoint paths
• construct graphs with k+1
edge-disjoint paths from
graphs with k edge-disjoint
paths
• repeat
128. 129
FFSM (Huan, et al. ICDM’03)
Represent graphs using canonical adjacency matrix
(CAM)
Join two CAMs or extend a CAM to generate a new
graph
Store the embeddings of CAMs
All of the embeddings of a pattern in the database
Can derive the embeddings of newly generated CAMs
129. 130
MoFa (Borgelt and Berthold ICDM’02)
Extend graphs by adding a new edge
Store embeddings of discovered frequent graphs
Fast support calculation
Also used in other later developed algorithms
such as FFSM and GASTON
Expensive Memory usage
Local structural pruning