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Renowned Speakers

Aliyu Usman Ahmad

Aliyu Usman Ahmad

University of Aberdeen UK

Daniele Menezes Nascimento

Daniele Menezes Nascimento

Mc Gill University Canada

En-Bing Lin

En-Bing Lin

Central Michigan University USA

Fairouz Kamareddine

Fairouz Kamareddine

Heriot-Watt University UK

Fionn Murtagh

Fionn Murtagh

University of Huddersfield UK

Han-joon Kim,

Han-joon Kim,

University of Seoul South Korea

Nikolaos Freris,

Nikolaos Freris,

New York University Abu Dhabi, UAE

Ruben Jerves cobo

Ruben Jerves cobo

Ghent University, Belgium

Data Mining 2024

About Conference

The 11th International Conference on Big Data Analysis and Data Mining is going to be held on October 17-18, 2024 in London, UK. is a premier event that brings together leading experts, researchers, and practitioners from around the world to discuss the latest advancements, challenges, and opportunities in the field of big data analysis and data mining. This two-day conference aims to foster collaboration, knowledge sharing, and innovation in this rapidly evolving domain. Engage in interactive workshops and hands-on sessions designed to provide practical skills and knowledge in areas such as data visualization, deep learning, data security, and the use of popular tools like R and Tableau. Join thought-provoking panel discussions where experts will debate current issues, share diverse perspectives, and explore future directions in big data analysis and data mining. Take advantage of numerous networking opportunities, including industry panels, round table discussions, poster sessions, and exhibitor booths. These events are designed to facilitate collaboration and foster professional connections among attendees. Discover innovative research and emerging ideas during the poster presentation sessions, where researchers and students will showcase their latest findings and receive feedback from peers and experts.

Who Should Attend?

Data Scientists and Analysts

Machine Learning Engineers

Researchers and Academics

Industry Professionals and Practitioners

Students and Early Career Researchers

Technology Developers and Vendors

Policy Makers and Regulators

Sessions and Tracks

Track 1 : Foundations of Big Data Analysis

Foundations of Big Data Analysis is a foundational session designed to provide participants with a thorough understanding of the core concepts and principles that underpin the field of big data analytics. This session covers essential topics such as the characteristics of big data, data acquisition, storage, and pre-processing, as well as data exploration techniques. Participants will learn about the challenges and opportunities presented by big data and gain insight into how to effectively analyze and derive valuable insights from large and complex datasets. Through engaging lectures and interactive discussions, this session aims to equip participants with the knowledge and skills needed to navigate the world of big data analytics confidently.

Track 2 : Big Data Technologies and Tools session

Big Data Technologies and Tools session  offers a comprehensive overview of the latest technologies and tools used in the field of big data analytics. Participants will learn about various platforms, frameworks, and software solutions that are essential for processing, storing, and analyzing large volumes of data. The session covers a wide range of topics, including distributed computing; cloud computing, NoSQL databases, Hadoop, Spark, and machine learning libraries. Through hands-on demonstrations and practical examples, participants will gain a deep understanding of how these technologies and tools can be applied to real-world big data challenges. Whether you are a beginner or an experienced data professional, this session will provide you with valuable insights into the rapidly evolving landscape of big data technologies.

Track 3 : Data Cleaning and Preprocessing

Data Cleaning and Preprocessing is a critical process in the field of data analysis, especially when dealing with large and complex datasets. This session provides participants with an in-depth understanding of the importance of data cleaning and preprocessing and the various techniques involved.Participants will learn how to identify and handle missing or duplicate data, remove outliers, and standardize data formats. They will also explore techniques for data transformation, such as normalization and encoding categorical variables. Through hands-on exercises and real-world examples, participants will gain practical skills in cleaning and preprocessing data to ensure its quality and suitability for analysis.By the end of the session, participants will have a solid foundation in data cleaning and preprocessing principles and techniques, enabling them to effectively prepare data for further analysis and interpretation.

Track 4 : Clustering and Association Rule Mining

Clustering and Association Rule Mining are advanced techniques used in data mining to uncover patterns and relationships within datasets. In the Clustering portion of this session, participants will learn about different clustering algorithms, such as K-means and hierarchical clustering, and how to apply them to group similar data points together. They will also explore practical applications of clustering, such as customer segmentation and anomaly detection.In the Association Rule Mining portion, participants will delve into the theory and practice of discovering interesting relationships between variables in large datasets. They will learn about popular algorithms like Apriori and FP-growth, and how to interpret and apply the resulting rules to make data-driven decisions.Through hands-on exercises and real-world examples, participants will gain a deep understanding of these powerful techniques and how to leverage them to extract valuable insights from their data.

Track 5 : Text Mining and Natural Language Processing

Text Mining and Natural Language Processing (NLP) are essential techniques for extracting insights and knowledge from unstructured text data. In this session, participants will learn about the principles and applications of text mining and NLP in various fields.The Text Mining portion of the session will cover topics such as text preprocessing, tokenization, and feature extraction. Participants will also explore techniques for sentiment analysis, topic modeling, and text classification using machine learning algorithms.In the Natural Language Processing portion, participants will learn about the fundamentals of NLP, including syntax and semantics. They will also explore advanced NLP techniques such as named entity recognition, part-of-speech tagging, and text summarization

Track 6: Exploratory Data Analysis (EDA)

Exploratory Data Analysis is a critical step in the data analysis process that involves analyzing and visualizing data to understand its key characteristics, uncover patterns, and identify potential relationships between variables. In this session, participants will learn about the principles and techniques of EDA and how to apply them to real-world datasets.Participants will explore methods for summarizing and visualizing data, such as histograms, box plots, and scatter plots. They will also learn how to identify outliers, missing values, and other data issues that may impact the analysis.Through hands-on exercises and case studies, participants will gain practical experience in conducting EDA and interpreting the results. By the end of the session, participants will have a solid understanding of EDA principles and how to apply them to gain valuable insights from data.

Track 7: Machine Learning for Big Data

 Machine Learning for Big Data  is a session that focuses on the application of machine learning techniques to large and complex datasets. Participants will learn about the principles of machine learning and how to apply them to solve real-world problems in big data analytics.The session covers a range of machine learning topics, including supervised learning, unsupervised learning, and reinforcement learning. Participants will explore different machine learning algorithms and models, such as decision trees, support vector machines, and neural networks, and learn how to select the most appropriate model for a given problem.Through hands-on exercises and case studies, participants will gain practical experience in building and evaluating machine learning models using big data. By the end of the session, participants will have the skills and knowledge needed to apply machine learning techniques to big data analytics projects effectively.

Track 8 : Future Trends in Big Data Analysis and Data Mining

Future Trends in Big Data Analysis and Data Mining explores emerging technologies and methodologies that are shaping the future of data analysis. Advancements in AI and ML are expected to play a significant role in the future of data analysis, enabling more sophisticated and automated analysis techniques. Deep learning, a subset of ML, is expected to continue to grow in importance, particularly in areas such as image and speech recognition. With the rise of IoT devices, edge computing is becoming increasingly important for processing data closer to the source, reducing latency and bandwidth usage. As data collection and analysis become more pervasive, there is a growing focus on data privacy and ethical considerations in data mining and analysis. As datasets continue to grow in size and complexity, effective data visualization techniques will be crucial for making sense of the data and communicating insights.

Track 9Real Time Big Data Processing

Real-time processing has benefits across all industries in today’s markets. With a growing focus on Big Data, this system of processing and acquiring insights can drive enterprises to new levels of achievement.Some real-world applications of real-time processing are found in banking systems, data streaming, customer service structures, and weather radars. Without real-time processing, these industries would not be possible or would deeply lack accuracy.For example, weather radar is heavily reliant on the real-time insights provided by this system of data processing. Due to the sheer volume of data that is being collected by supercomputers to study weather interactions and predictions, real-time processing is absolutely critical to successful interpretation. Solve the most complex problems and answer the biggest questions with HPE high performance computing solutions, expertise, and global partner ecosystem. The power of supercomputing with HPE allows enterprise organizations to scale up or scale out, on-premises or in the cloud, with intentional storage and software to power your innovation. Every workload, aligned to your budget.HPE Green Lake for HPC powers fast deployment with ease, for all your consumption-based projects. 

Track 10 Deep Learning for Big Data Applications

Typically, training deep neural networks requires large amounts of data that often do not fit in memory. You do not need multiple computers to solve problems using data sets too large to fit in memory. Instead, you can divide your training data into mini-batches that contain a portion of the data set. By iterating over the mini-batches, networks can learn from large data sets without needing to load all data into memory at once. If your data is too large to fit in memory, use a data store to work with mini-batches of data for training and inference. MATLAB® provides many different types of data store tailored for different applications. For more information about data stores for different applications, see Datastores for Deep Learning. augmentedImageDatastore is specifically designed to preprocess and augment batches of image data for machine learning and computer vision applications. For more information, see Preprocess Images for Deep Learning.Deep Learning for Big Data Applications is poised to drive significant innovations across various industries by harnessing the power of deep neural networks to analyze and derive insights from large datasets.

Track 11 : Social Network Analysis

Social network analysis is the process of investigating social structures through the use of networks and graph theory. This article introduces data scientists to the theory of social networks, with a short introduction to graph theory and information spread. It dives into Python code with NetworkX constructing and implying social networks from real datasets. Nodes (A, B,C,D,E in the example) are usually representing entities in the network, and can hold self-properties (such as weight, size, position and any other attribute) and network-based properties (such as Degree- number of neighbors or Cluster- a connected component the node belongs to etc.).Edges represent the connections between the nodes, and might hold properties as well (such as weight representing the strength of the connection, direction in case of asymmetric relation or time if applicable). These two basic elements can describe multiple phenomena, such as social connections, virtual routing network, physical electricity networks, roads network, biology relations network and many other relationships

Track 12Big Data Security and Privacy

Big Data Secuirty and Privacy  As the volume of data generated and processed by organizations continues to grow exponentially, ensuring the security and privacy of big data has become a critical concern. Big Data Security and Privacy encompasses a range of strategies, technologies, and practices designed to protect sensitive information from unauthorized access, breaches, and other cyber threats. This field addresses the unique challenges posed by the vast scale, variety, and velocity of big data. Implementing systems to detect and prevent unauthorized access and anomalies in real-time.Implementing systems to detect and prevent unauthorized access and anomalies in real-time. Protecting a wide variety of data types, including structured, unstructured, and semi-structured data, presents unique challenges. Managing the complexity of big data environments, including cloud and hybrid infrastructures, requires advanced security solutions. Big Data Security and Privacy is essential for protecting sensitive information in an increasingly data-driven world. By implementing robust security measures and staying informed about emerging threats, organizations can safeguard their data assets and maintain the trust of their customers and stakeholders.

Track 13 Recommender Systems and Personalization 

Recommender Systems and Personalization technologies are designed to enhance user experience by providing tailored recommendations and content based on individual preferences and behavior. These systems analyze user data, such as past interactions, preferences, and demographics, to generate personalized recommendations for products, services, or content.Recommender Systems utilize various algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches, to suggest items that are likely to be of interest to the user. These systems are widely used in e-commerce platforms, streaming services, social media platforms, and more, to help users discover new products, movies, music, or news articles.Personalization goes beyond recommendations to customize user interfaces, content, and services based on individual preferences. This can include personalized product offerings, targeted marketing campaigns, and tailored user experiences. By leveraging data analytics and machine learning techniques, organizations can create more engaging and relevant experiences for their users, ultimately driving user engagement, satisfaction, and loyalty.

Track 14 : Graph Mining and Network Analysis 

Graph Mining and Network Analysis are fields of study that focus on extracting valuable insights from graph-structured data. Graphs are mathematical structures that represent relationships between entities, with nodes representing entities and edges representing relationships between them.Graph Mining involves applying data mining techniques to analyze large-scale graphs to discover patterns, structures, and trends. This can include identifying communities or clusters of nodes, detecting anomalies or outliers, and predicting missing links or future connections.Network Analysis, on the other hand, focuses on the study of networks to understand their structure, dynamics, and properties. It involves analyzing network topologies, centrality measures, and connectivity patterns to gain insights into the behavior of complex systems.Applications of Graph Mining and Network Analysis are diverse and include social network analysis, biological network analysis, transportation network analysis, and more. These techniques are used in various fields such as social sciences, biology, cybersecurity, and telecommunications to uncover hidden patterns and relationships that are not apparent in traditional data analysis methods

Track 15Privacy-Preserving Data Mining

Privacy-Preserving Data Mining (PPDM) is a field of study that focuses on developing techniques and algorithms to extract useful information from data while preserving the privacy of individuals whose data is being analyzed. Adding noise to the data to mask sensitive information while maintaining the overall statistical properties of the dataset. Removing or obfuscating personally identifiable information (PII) from the dataset to prevent the identification of individuals. Ensuring that the output of a data analysis algorithm does not reveal information about any individual data point in the dataset.PPDM is particularly important in contexts where sensitive information needs to be protected, such as healthcare, finance, and social media. Traditional data mining techniques may inadvertently reveal sensitive information about individuals, leading to privacy breaches and ethical concerns.

Track 16Stream Data Mining and Sensor Data Analysis

Stream Data Mining and Sensor Data Analysis are fields of study focused on extracting knowledge from continuous data streams generated by sensors and other data sources in real-time.Stream Data Mining involves the application of data mining techniques to analyze and extract patterns, trends, and insights from high-velocity data streams. This requires algorithms that can process data on-the-fly, often in limited memory and with stringent time constraints. Sensor Data Analysis, on the other hand, specifically focuses on analysing data from sensors, which are devices that measure physical or environmental conditions. This includes data from IoT devices, smart devices, and industrial sensors, among others. Sensor data analysis involves processing, interpreting, and visualizing sensor data to extract useful information for various applications such as environmental monitoring, healthcare, and industrial automation. Both Stream Data Mining and Sensor Data Analysis play crucial roles in enabling real-time decision-making, monitoring of systems, and detecting anomalies or patterns that may indicate important events or changes in the environment. These techniques are essential for applications where timely insights from streaming data can lead to significant improvements in efficiency, safety, and effectiveness.

Track 17 : Big Data Analytics in Finance and Banking

Big Data Analytics has significantly transformed the finance and banking sector by enabling institutions to extract valuable insights from vast and varied datasets. This analytical approach involves the use of advanced tools and techniques to analyze complex data sets, including customer transactions, market trends, and social media interactions, among others. By analyzing historical data and market trends, financial institutions can better assess and manage risks associated with lending, investments, and market volatility. Big Data Analytics helps in detecting patterns and anomalies in transactions, enabling institutions to identify and prevent fraudulent activities in real-time. By analyzing customer data, including transaction histories and interactions, banks can personalize offerings, improve customer service, and enhance customer retention. Big Data Analytics enables banks to streamline operations, optimize processes, and reduce costs by identifying inefficiencies and bottlenecks. Overall, Big Data Analytics plays a crucial role in helping finance and banking institutions gain deeper insights, improve decision-making, and enhance customer experiences in an increasingly competitive and data-driven environment

Track 18Case studies and best practices in Big Data Analytics 

Case studies and best practices in Big Data Analytics offer valuable insights into how organizations can effectively leverage data to drive business value and achieve strategic objectives. These examples showcase real-world applications of Big Data Analytics across various industries and highlight successful strategies and approaches that have delivered measurable results.One example of a case study in Big Data Analytics is how a retail company used customer data to optimize its marketing campaigns. By analyzing customer demographics, purchasing behavior, and interactions with marketing materials, the company was able to tailor its campaigns to target specific customer segments more effectively, resulting in increased sales and customer satisfaction.Another example is how a healthcare organization used Big Data Analytics to improve patient outcomes. By analyzing patient data from electronic health records, diagnostic tests, and treatments, the organization was able to identify patterns and trends that helped doctors make more accurate diagnoses and develop personalized treatment plans, leading to better patient outcomes and reduced healthcare costs

Market Analysis

The global market for Big Data Analysis and Data Mining has witnessed significant growth from 2020 to 2028, driven by advancements in technology, the increasing volume of data generated across industries, and the rising demand for data-driven decision-making. This period has seen a surge in the adoption of big data technologies and analytics solutions, transforming the way organizations operate and compete in the market. In 2020 The market was valued at approximately $138.9 billion. From 2021-2024 The market experienced a compound annual growth rate (CAGR) of around 10.6%, reaching an estimated value of $187.4 billion by the end of 2024.From The market is projected to continue its robust growth, with an expected CAGR of 11.5%, reaching approximately $324.3 billion by 2028.  In Key Drivers the exponential growth in data generated from various sources such as social media, IoT devices, and enterprise applications has fueled the demand for advanced data analytics solutions. Innovations in machine learning, artificial intelligence, and cloud computing have enhanced the capabilities and scalability of big data analytics. Organizations increasingly rely on data-driven insights to enhance operational efficiency, customer experiences, and strategic decision-making.

North America: Dominates the market due to the presence of major technology companies, high adoption rates of advanced analytics, and significant investments in R&D.

Europe: Follows North America, driven by strong regulatory frameworks and substantial investments in data protection and analytics technologies.

Asia-Pacific: Expected to witness the highest growth rate due to rapid digital transformation, increasing internet penetration, and growing awareness of the benefits of big data analytics.

Latin America and Middle East & Africa: Emerging markets with significant potential for growth as they adopt new technologies and improve their IT infrastructure.

The market for Big Data Analysis and Data Mining is poised for continued growth and innovation. Organizations across various sectors are increasingly recognizing the strategic importance of big data analytics in driving business value. The 11th International Conference on Big Data Analysis and Data Mining will provide a critical platform for industry leaders, researchers, and practitioners to share insights, discuss challenges, and explore future trends in this dynamic field.

Past Conference Report

Data Mining 2023

The 10th International Conference on Big Data Analysis and Data Mining (Data Mining 2023) which is going to be held during June 14 -15, 2023 at Edinburgh, Scotland to share the knowledge. The main theme of the conference is “Moving beyond the analyses of Data Science".This conference aimed to expand its coverage in the areas of Big Data and Data Mining where expert talks, young researcher’s presentations will be placed in every session of the meeting will be inspired and keep up your enthusiasm. We feel our expert Organizing Committee is our major asset. However, your presence over the venue will add one more feather to the crown of Data Mining 2023.Data Mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data Mining 2023 is comprised of the following sessions with 27 tracks and 160 sub-sessions designed to offer comprehensive sessions that address current issues of Big Data and Data Mining. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time-consuming to resolve.

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Conference Date October 17-18, 2024

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Past Conference Report

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  • Anomaly Detection
  • Artificial Intelligence
  • Association Rule Mining
  • Big Data
  • Business Intelligence
  • Clustering
  • Customer Analytics
  • Data Analytics Platforms
  • Data Cleansing
  • Data Exploration
  • Data Fusion
  • Data Governance
  • Data Integration
  • Data Lakes
  • Data Management
  • Data Mining
  • Data Mining Algorithms
  • Data Mining Applications
  • Data Mining Techniques
  • Data Mining Tools
  • Data Preprocessing
  • Data Privacy
  • Data Quality
  • Data Science
  • Data Security
  • Data Storage
  • Data Visualization
  • Data Warehousing
  • Data-driven Decision Making
  • Decision Trees
  • Deep Learning
  • Dimensionality Reduction
  • Feature Selection
  • Fraud Detection
  • Hadoop
  • Healthcare Analytics
  • Machine Learning
  • Machine Learning Models
  • Natural Language Processing
  • Neural Networks
  • Optimization Algorithms
  • Pattern Recognition
  • Predictive Analytics
  • Real-time Analytics
  • Regression Analysis
  • Sentiment Analysis
  • Social Media Analytics
  • Spark
  • Streaming Analytics
  • Text Mining