Analyze the market segmentation of the Data Mining Tools industry. Gain insights into market share distribution with a detailed breakdown of key segments and their growth.

This dataset is about books. It has 1 row and is filtered where the book is Data mining techniques in CRM : inside customer segmentation. It features 7 columns including author, publication date, language, and book publisher.

Data Mining and Modeling Market Outlook

The global data mining and modeling market size was valued at approximately $28.5 billion in 2023 and is projected to reach $70.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. This remarkable growth can be attributed to the increasing complexity and volume of data generated across various industries, necessitating robust tools and techniques for effective data analysis and decision-making processes.

One of the primary growth factors driving the data mining and modeling market is the exponential increase in data generation owing to advancements in digital technology. Modern enterprises generate extensive data from numerous sources such as social media platforms, IoT devices, and transactional databases. The need to make sense of this vast information trove has led to a surge in the adoption of data mining and modeling tools. These tools help organizations uncover hidden patterns, correlations, and insights, thereby enabling more informed decision-making and strategic planning.

Another significant growth driver is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies. Data mining and modeling are critical components of AI and ML algorithms, which rely on large datasets to learn and make predictions. As businesses strive to stay competitive, they are increasingly investing in AI-driven analytics solutions. This trend is particularly prevalent in sectors such as healthcare, finance, and retail, where predictive analytics can provide a substantial competitive edge. Moreover, advancements in big data technologies are further bolstering the capabilities of data mining and modeling solutions, making them more effective and efficient.

The burgeoning demand for business intelligence (BI) and analytics solutions is also a major factor propelling the market. Organizations are increasingly recognizing the value of data-driven insights in identifying market trends, customer preferences, and operational inefficiencies. Data mining and modeling tools form the backbone of sophisticated BI platforms, enabling companies to transform raw data into actionable intelligence. This demand is further amplified by the growing importance of regulatory compliance and risk management, particularly in highly regulated industries such as banking, financial services, and healthcare.

From a regional perspective, North America currently dominates the data mining and modeling market, owing to the early adoption of advanced technologies and the presence of major market players. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation initiatives and increasing investments in AI and big data technologies. Europe also holds a significant market share, supported by stringent data protection regulations and a strong focus on innovation.

Component Analysis

The data mining and modeling market by component is broadly segmented into software and services. The software segment encompasses various tools and platforms that facilitate data mining and modeling processes. These software solutions range from basic data analysis tools to advanced platforms integrated with AI and ML capabilities. The increasing complexity of data and the need for real-time analytics are driving the demand for sophisticated software solutions. Companies are investing in custom and off-the-shelf software to enhance their data handling and analytical capabilities, thereby gaining a competitive edge.

The services segment includes consulting, implementation, training, and support services. As organizations strive to leverage data mining and modeling tools effectively, the demand for professional services is on the rise. Consulting services help businesses identify the right tools and strategies for their specific needs, while implementation services ensure the seamless integration of these tools into existing systems. Training services are crucial for building in-house expertise, enabling teams to maximize the benefits of data mining and modeling solutions. Support services ensure the ongoing maintenance and optimization of these tools, addressing any technical issues that may arise.

The software segment is expected to dominate the market throughout the forecast period, driven by continuous advancements in te

The global Data Mining Software market is experiencing robust growth, driven by the increasing need for businesses to extract valuable insights from massive datasets. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. The burgeoning adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both large enterprises and SMEs. Furthermore, advancements in machine learning and artificial intelligence algorithms are enhancing the accuracy and efficiency of data mining processes, leading to better decision-making across various sectors like finance, healthcare, and marketing. The rise of big data analytics and the increasing availability of affordable, high-powered computing resources are also significant contributors to market growth. However, the market faces certain challenges. Data security and privacy concerns remain paramount, especially with the increasing volume of sensitive information being processed. The complexity of data mining software and the need for skilled professionals to operate and interpret the results present a barrier to entry for some businesses. The high initial investment cost associated with implementing sophisticated data mining solutions can also deter smaller organizations. Nevertheless, the ongoing technological advancements and the growing recognition of the strategic value of data-driven decision-making are expected to overcome these restraints and propel the market toward continued expansion. The market segmentation reveals a strong preference for cloud-based solutions, reflecting the industry's trend toward flexible and scalable IT infrastructure. Large enterprises currently dominate the market share, but SMEs are rapidly adopting data mining software, indicating promising future growth in this segment. Geographic analysis shows that North America and Europe are currently leading the market, but the Asia-Pacific region is poised for significant growth due to increasing digitalization and economic expansion in countries like China and India.

Data Mining Software Market Outlook

The global data mining software market size was valued at USD 7.2 billion in 2023 and is projected to reach USD 15.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.7% during the forecast period. This growth is driven primarily by the increasing adoption of big data analytics and the rising demand for business intelligence across various industries. As businesses increasingly recognize the value of data-driven decision-making, the market is expected to witness substantial growth.

One of the significant growth factors for the data mining software market is the exponential increase in data generation. With the proliferation of internet-enabled devices and the rapid advancement of technologies such as the Internet of Things (IoT), there is a massive influx of data. Organizations are now more focused than ever on harnessing this data to gain insights, improve operations, and create a competitive advantage. This has led to a surge in demand for advanced data mining tools that can process and analyze large datasets efficiently.

Another driving force is the growing need for personalized customer experiences. In industries such as retail, healthcare, and BFSI, understanding customer behavior and preferences is crucial. Data mining software enables organizations to analyze customer data, segment their audience, and deliver personalized offerings, ultimately enhancing customer satisfaction and loyalty. This drive towards personalization is further fueling the adoption of data mining solutions, contributing significantly to market growth.

The integration of artificial intelligence (AI) and machine learning (ML) technologies with data mining software is also a key growth factor. These advanced technologies enhance the capabilities of data mining tools by enabling them to learn from data patterns and make more accurate predictions. The convergence of AI and data mining is opening new avenues for businesses, allowing them to automate complex tasks, predict market trends, and make informed decisions more swiftly. The continuous advancements in AI and ML are expected to propel the data mining software market over the forecast period.

Regionally, North America holds a significant share of the data mining software market, driven by the presence of major technology companies and the early adoption of advanced analytics solutions. The Asia Pacific region is also expected to witness substantial growth due to the rapid digital transformation across various industries and the increasing investments in data infrastructure. Additionally, the growing awareness and implementation of data-driven strategies in emerging economies are contributing to the market expansion in this region.

Text Mining Software is becoming an integral part of the data mining landscape, offering unique capabilities to analyze unstructured data. As organizations generate vast amounts of textual data from various sources such as social media, emails, and customer feedback, the need for specialized tools to extract meaningful insights is growing. Text Mining Software enables businesses to process and analyze this data, uncovering patterns and trends that were previously hidden. This capability is particularly valuable in industries like marketing, customer service, and research, where understanding the nuances of language can lead to more informed decision-making. The integration of text mining with traditional data mining processes is enhancing the overall analytical capabilities of organizations, allowing them to derive comprehensive insights from both structured and unstructured data.

Component Analysis

The data mining software market is segmented by components, which primarily include software and services. The software segment encompasses various types of data mining tools that are used for analyzing and extracting valuable insights from raw data. These tools are designed to handle large volumes of data and provide advanced functionalities such as predictive analytics, data visualization, and pattern recognition. The increasing demand for sophisticated data analysis tools is driving the growth of the software segment. Enterprises are investing in these tools to enhance their data processing capabilities and derive actionable insights.

Within the software segment, the emergence of cloud-based data mining solutions is a notable trend. Cloud-based solutions offer several advantages, including s

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

The size and share of this market is categorized based on Data Mining Software (Text Mining, Statistical Analysis, Predictive Modeling, Machine Learning, Data Integration) and Data Visualization Software (Dashboard Creation, Graphical Representation, Real-Time Data Visualization, Interactive Reporting, 3D Visualization) and Application Areas (Pharmaceutical Research, Genomics and Proteomics, Clinical Data Analysis, Healthcare Analytics, Biotechnology) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

Data for the paper "Segmentation of Argumentative Texts by Key Statements for Argument Mining from the Web". This page is currently anonymized as the paper is under blind review.

Contains the input arguments (as text or HTML), segments created by GPT-4 and PaLM, the ground truth key statements and the PaLM segments manually matched to the ground truth key statements.

This record contains the datasets and models used and produced for the work reported in the paper "Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers" (link).

Please cite this paper if you are using the models/datasets or find it relevant to your research:

@article{barman_combining_2020,
  title = {{Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers}},
  author = {Raphaël Barman and Maud Ehrmann and Simon Clematide and Sofia Ares Oliveira and Frédéric Kaplan},
  journal= {Journal of Data Mining \& Digital Humanities},
  volume= {HistoInformatics}
  DOI = {10.5281/zenodo.4065271},
  year = {2021},
  url = {https://jdmdh.episciences.org/7097},
}

Please note that this record contains data under different licenses.

1. DATA

• Annotations (json files): JSON files contains image annotations, with one file per newspaper containing region annotations (label and coordinates) in VIA format. The following licenses apply:
  • luxwort.json: those annotations are under a CC0 1.0 license. Please refer to the right statement specified for each image in the file.
  • GDL.json, IMP.json and JDG.json: those annotations are under a CC BY-SA 4.0 license.

• Image files: The archive images.zip contains the Swiss titles image files (GDL, IMP, JDG) used for the experiments described in the paper. Those images are under copyright (property of the journal Le Temps and of ArcInfo) and can be used for academic research or educational purposes only. Redistribution, publication or commercial use are not permitted. These terms of use are similar to the following right statement: http://rightsstatements.org/vocab/InC-EDU/1.0/

2. MODELS

Some of the best models are released under a CC BY-SA 4.0 license (they are also available as assets of the current Github release).

• JDG_flair-FT: this model was trained on JDG using french Flair and FastText embeddings. It is able to predict the four classes presented in the paper (Serial, Weather, Death notice and Stocks).
• Luxwort_obituary_flair-bpemb: this model was trained on Luxwort using multilingual Flair and Byte-pair embeddings. It is able to predict the Death notice class.
• Luxwort_obituary_flair-FT_indomain: this model was trained on Luxwort using in-domain Flair and FastText embeddings (trained on Luxwort data). It is also able to predict the Death notice class.

Those models can be used to predict probabilities on new images using the same code as in the original dhSegment repository. One needs to adjust three parameters to the predict function: 1) embeddings_path (the path to the embeddings list), 2) embeddings_map_path(the path to the compressed embedding map), and 3) embeddings_dim (the size of the embeddings).

Please refer to the paper for further information or contact us.

3. CODE:

https://github.com/dhlab-epfl/dhSegment-text

4. ACKNOWLEDGEMENTS
We warmly thank the journal Le Temps (owner of La Gazette de Lausanne and the Journal de Genève) and the group ArcInfo (owner of L'Impartial) for accepting to share the related datasets for academic purposes. We also thank the National Library of Luxembourg for its support with all steps related to the Luxemburger Wort annotation release.
This work was realized in the context of the impresso - Media Monitoring of the Past project and supported by the Swiss National Science Foundation under grant CR- SII5_173719.

5. CONTACT
Maud Ehrmann (EPFL-DHLAB)
Simon Clematide (UZH)

Big Data Tools Market Outlook

The global Big Data Tools market size is anticipated to grow from USD 31.5 billion in 2023 to USD 103.5 billion by 2032, at a compound annual growth rate (CAGR) of 14.5%. This robust growth can be attributed to several key factors, including the increasing volume of data generated across various industries, advancements in data analytics technologies, and the growing demand for data-driven decision-making. The proliferation of IoT devices, the rise of artificial intelligence, and the emphasis on enhancing customer experience further drive the expansion of the Big Data Tools market worldwide.

The exponential increase in data generation is one of the foremost drivers of the Big Data Tools market. With the rise of digital transformation initiatives, industries are generating massive amounts of data every second. From social media interactions to transactional data and from IoT sensors to operational data, the volume, variety, and velocity of data have escalated to unprecedented levels. Organizations are increasingly recognizing the potential of leveraging this data to gain actionable insights, optimize operations, and drive business growth, thus fueling the demand for advanced Big Data tools and technologies.

Another significant growth factor is the technological advancements in data analytics and machine learning. Big Data tools have evolved from traditional data warehousing and analytics platforms to sophisticated solutions incorporating artificial intelligence and machine learning. These advancements enable organizations to perform predictive and prescriptive analytics, uncover hidden patterns, and make data-driven decisions with greater accuracy and speed. The continuous innovation and integration of advanced technologies into Big Data tools are propelling their adoption across various sectors.

The increasing emphasis on enhancing customer experience is also driving the Big Data Tools market. Businesses are leveraging Big Data analytics to gain deeper insights into customer behavior, preferences, and sentiment. By analyzing this data, organizations can personalize their offerings, improve customer engagement, and deliver superior experiences. In sectors such as retail, banking, and healthcare, the ability to understand and predict customer needs has become a competitive differentiator, leading to significant investments in Big Data tools to achieve these objectives.

Data Mining Tools play a pivotal role in the Big Data landscape by enabling organizations to extract valuable insights from vast datasets. These tools are designed to sift through large volumes of data, identify patterns, and uncover relationships that might not be immediately apparent. By leveraging advanced algorithms and statistical techniques, Data Mining Tools help businesses make informed decisions, optimize processes, and enhance strategic planning. As the volume of data continues to grow exponentially, the demand for robust and efficient Data Mining Tools is on the rise, driving innovation and competition in the market. Companies are increasingly investing in these tools to gain a competitive edge and unlock the full potential of their data assets.

From a regional perspective, North America is expected to dominate the Big Data Tools market, primarily due to the presence of leading technology companies, early adoption of advanced analytics solutions, and significant investments in data-driven initiatives. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digitalization of economies, increasing internet penetration, and the burgeoning e-commerce sector are driving the demand for Big Data tools in this region. Additionally, governments in countries like China and India are promoting data analytics and AI, further boosting the market's growth prospects.

Component Analysis

The Big Data Tools market is segmented by component into software and services. The software segment includes various types of Big Data platforms and analytics tools. These software solutions are designed to handle, process, and analyze large volumes of structured and unstructured data. Key offerings within this segment include data storage solutions, data processing frameworks, data visualization tools, and advanced analytics software. The continuous innovation in software capabilities, such as real-time data analytics and AI integration, is driving the growth of this segment.

The brain-like computer market is experiencing significant growth, driven by advancements in neuromorphic computing and the increasing demand for high-performance, energy-efficient computing solutions in diverse sectors. The market, estimated at $2 billion in 2025, is projected to exhibit a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated market value of $10 billion by 2033. Key application areas include data mining and research, with the segment utilizing neurons exceeding 100 million units expected to dominate due to their enhanced processing capabilities. Leading players like Intel, IBM, and prominent universities are heavily investing in R&D, fueling innovation and market expansion. The North American region currently holds a significant market share, attributed to substantial technological advancements and early adoption of brain-like computing technologies, followed by Europe and Asia Pacific. However, high development costs and the complexity involved in designing and implementing these systems present significant challenges to widespread adoption. Future growth is likely to be fueled by continuous technological advancements, decreasing production costs, and the growing demand for artificial intelligence and machine learning applications requiring superior processing speed and energy efficiency. The market segmentation reveals a strong correlation between the number of neurons and application type. Larger-scale neuronal systems (above 100 million units) are predominantly employed in demanding applications like data mining, where the capability to process vast datasets is crucial. Conversely, smaller-scale systems might be suitable for specific research tasks with less complex computational requirements. Regional disparities are expected to persist, with North America maintaining its leadership position while Asia Pacific demonstrates significant growth potential due to increasing investments in research and development and the expanding adoption of AI/ML technologies across various sectors. Continued advancements in materials science, allowing for greater miniaturization and energy efficiency, will further drive market growth in the coming years.

The Data Mining Tools market is an essential component of today's data-driven landscape, enabling organizations to extract valuable insights from vast amounts of data generated every day. These tools facilitate the analysis of complex datasets, helping businesses make informed decisions, identify trends, and im

The global market size of Mining Laboratory Automation Solutions is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
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There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
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The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

Global Predictive Analytics Market size worth at USD 16.19 Billion in 2023 and projected to USD 113.8 Billion by 2032, with a CAGR of around 24.19% between 2024-2032.

Clustering is a fundamental tool in data mining, widely used in various fields such as image segmentation, data science, pattern recognition, and bioinformatics. Density Peak Clustering (DPC) is a density-based method that identifies clusters by calculating the local density of data points and selecting cluster centers based on these densities. However, DPC has several limitations. First, it requires a cutoff distance to calculate local density, and this parameter varies across datasets, which requires manual tuning and affects the algorithm’s performance. Second, the number of cluster centers must be manually specified, as the algorithm cannot automatically determine the optimal number of clusters, making the algorithm dependent on human intervention. To address these issues, we propose an adaptive Density Peak Clustering (DPC) method, which automatically adjusts parameters like cutoff distance and the number of clusters, based on the Delaunay graph. This approach uses the Delaunay graph to calculate the connectivity between data points and prunes the points based on these connections, automatically determining the number of cluster centers. Additionally, by optimizing clustering indices, the algorithm automatically adjusts its parameters, enabling clustering without any manual input. Experimental results on both synthetic and real-world datasets demonstrate that the proposed algorithm outperforms similar methods in terms of both efficiency and clustering accuracy.

One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant, or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. A new method for key gene selection has been proposed on the basis of interval segmentation purity that is defined as the purity of samples belonging to a certain class in intervals segmented by a mode search algorithm. This method identifies key variables most discriminative for each class, which offers possibility of unraveling the biological implication of selected genes. A salient advantage of the new strategy over existing methods is the capability of selecting genes that, though possibly exhibit a multimodal distribution, are the most discriminative for the classes of interest, considering that the expression levels of some genes may reflect systematic difference in within-class samples derived from different pathogenic mechanisms. On the basis of the key genes selected for individual classes, a support vector machine with block-wise kernel transform is developed for the classification of different classes. The combination of the proposed gene mining approach with support vector machine is demonstrated in cancer classification using two public data sets. The results reveal that significant genes have been identified for each class, and the classification model shows satisfactory performance in training and prediction for both data sets.

The Enterprise Data Warehouse (EDW) market, valued at $3,532 million in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 5.9% from 2025 to 2033. This growth is fueled by the increasing need for organizations to consolidate and analyze vast amounts of data from diverse sources to gain actionable insights for improved decision-making. Key drivers include the rising adoption of cloud-based EDW solutions, offering scalability, cost-effectiveness, and enhanced accessibility. The growing prevalence of big data and the demand for advanced analytics, particularly in sectors like healthcare and finance, further propel market expansion. Technological advancements, such as improved data integration capabilities and the emergence of artificial intelligence (AI) and machine learning (ML) in data analysis, are also significant contributors. While data security and privacy concerns pose some restraints, the overall market outlook remains positive, driven by the continuous digital transformation across industries and the imperative for data-driven strategies. The market segmentation reveals a strong preference for cloud-based EDW solutions, reflecting the industry's shift towards flexible and scalable infrastructure. Within applications, information processing and data mining segments dominate, highlighting the critical role of EDW in supporting core business operations and advanced analytical pursuits. Leading vendors like Teradata, Snowflake, and AWS are capitalizing on these trends, offering comprehensive solutions and driving innovation. Regional analysis indicates strong growth across North America and Europe, driven by high technology adoption and a mature market ecosystem. However, the Asia-Pacific region presents significant future potential, given its burgeoning digital economy and increasing investment in data infrastructure. The historical period (2019-2024) likely saw lower market size but experienced considerable growth to reach the 2025 figure, setting the stage for future expansion based on the projected CAGR.

The global designing data visualization services market is projected to reach a value of approximately USD 19.2 billion by 2033 from an estimated USD 10.7 billion in 2025, at a CAGR of 6.9% during the forecast period. The growth of the market is driven by the increasing need for data visualization tools to make sense of complex data, the growing popularity of cloud-based data visualization services, the increasing adoption of data visualization in various industries, and the technological advancements in data visualization. The market is segmented by type into dashboard software, data mining software, mobile business intelligence software, and predictive analytical software. The dashboard software segment is expected to dominate the market during the forecast period due to the increasing adoption of dashboards by businesses to monitor and track their performance. The data mining software segment is expected to grow at a significant CAGR during the forecast period due to the growing demand for data mining tools to extract valuable insights from large datasets. The mobile business intelligence software segment is expected to grow at a healthy CAGR during the forecast period due to the increasing adoption of mobile devices for business purposes. The predictive analytical software segment is expected to grow at a moderate CAGR during the forecast period due to the growing demand for predictive analytics tools to make informed decisions.