Data Mining Tools Market Outlook

According to our latest research, the global Data Mining Tools market size reached USD 1.93 billion in 2024, reflecting robust industry momentum. The market is expected to grow at a CAGR of 12.7% from 2025 to 2033, reaching a projected value of USD 5.69 billion by 2033. This growth is primarily driven by the increasing adoption of advanced analytics across diverse industries, rapid digital transformation, and the necessity for actionable insights from massive data volumes.

One of the pivotal growth factors propelling the Data Mining Tools market is the exponential rise in data generation, particularly through digital channels, IoT devices, and enterprise applications. Organizations across sectors are leveraging data mining tools to extract meaningful patterns, trends, and correlations from structured and unstructured data. The need for improved decision-making, operational efficiency, and competitive advantage has made data mining an essential component of modern business strategies. Furthermore, advancements in artificial intelligence and machine learning are enhancing the capabilities of these tools, enabling predictive analytics, anomaly detection, and automation of complex analytical tasks, which further fuels market expansion.

Another significant driver is the growing demand for customer-centric solutions in industries such as retail, BFSI, and healthcare. Data mining tools are increasingly being used for customer relationship management, targeted marketing, fraud detection, and risk management. By analyzing customer behavior and preferences, organizations can personalize their offerings, optimize marketing campaigns, and mitigate risks. The integration of data mining tools with cloud platforms and big data technologies has also simplified deployment and scalability, making these solutions accessible to small and medium-sized enterprises (SMEs) as well as large organizations. This democratization of advanced analytics is creating new growth avenues for vendors and service providers.

The regulatory landscape and the increasing emphasis on data privacy and security are also shaping the development and adoption of Data Mining Tools. Compliance with frameworks such as GDPR, HIPAA, and CCPA necessitates robust data governance and transparent analytics processes. Vendors are responding by incorporating features like data masking, encryption, and audit trails into their solutions, thereby enhancing trust and adoption among regulated industries. Additionally, the emergence of industry-specific data mining applications, such as fraud detection in BFSI and predictive diagnostics in healthcare, is expanding the addressable market and fostering innovation.

From a regional perspective, North America currently dominates the Data Mining Tools market owing to the early adoption of advanced analytics, strong presence of leading technology vendors, and high investments in digital transformation. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid industrialization, expansion of IT infrastructure, and growing awareness of data-driven decision-making in countries like China, India, and Japan. Europe, with its focus on data privacy and digital innovation, also represents a significant market share, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions modernize their operations and adopt cloud-based analytics solutions.

Component Analysis

The Component segment of the Data Mining Tools market is bifurcated into Software and Services. Software remains the dominant segment, accounting for the majority of the market share in 2024. This dominance is attributed to the continuous evolution of data mining algorithms, the proliferation of user-friendly graphical interfaces, and the integration of advanced analytics capabilities such as machine learning, artificial intelligence, and natural language pro

Explore the historical Whois records related to deep-data-mining.com (Domain). Get insights into ownership history and changes over time.

Data set for the paper What are the Effects of History Length and Age on Mining Software Change Impact? by Leon Moonen, Thomas Rolfsnes, David Binkley and Stefano di Alesio. In Journal of Empirical Software Engineering (EMSE), 2018, Springer. https://doi.org/10.1007/s10664-017-9588-z Available from https://evolveit.bitbucket.io/publications/emse2018/

Please cite this work by referring to the corresponding journal publication (a preprint is included in this package).

The goal of Software Change Impact Analysis is to identify artifacts (typically source-code files or individual methods therein) potentially affected by a change. Recently, there has been increased interest in mining software change impact based on evolutionary coupling. A particularly promising approach uses association rule mining to uncover potentially affected artifacts from patterns in the system’s change history. Two main considerations when using this approach are the history length, the number of transactions from the change history used to identify the impact of a change, and history age, the number of transactions that have occurred since patterns were last mined from the history. Although history length and age can significantly affect the quality of mining results, few guidelines exist on how to best select appropriate values for these two parameters.

In this paper, we empirically investigate the effects of history length and age on the quality of change impact analysis using mined evolutionary coupling. Specifically, we report on a series of systematic experiments using three state-of-the-art mining algorithms that involve the change histories of two large industrial systems and 17 large open source systems. In these experiments, we vary the length and age of the history used to mine software change impact, and assess how this affects precision and applicability. Results from the study are used to derive practical guidelines for choosing history length and age when applying association rule mining to conduct software change impact analysis.

Abandoned railroads and infrastructure from the anthracite coal mining industry are significant features in abandoned mine lands and are an important part of history; however, these features are often lost and masked by the passage of time and the regrowth of forests. The application of modern light detection and ranging (lidar) topographic analysis, combined with ground-truthing "boots on the ground" mapping, enable recovery of the location of these historical features. Waste rock piles and abandoned mine lands from historical mining locally appear as distinct features on the landscape depicted on the percent slope map. Abandoned, and in many places demolished, infrastructure such as breakers, turntables, rail beds, water tanks, tram piers, and bridge abutments, to name a few, were confirmed in the field and located with a global positioning system (GPS) receiver. This map captures the locations of many of the abandoned features from the coal mining industry near Forest City, Pennsylvania, and preserves a time that was an important part of the industrial revolution and a way of life that has been quiet for over half a century. The data layers should be used in conjunction with lidar data available separately at https://www.pasda.psu.edu.

According to Cognitive Market Research, the global Data Mining Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Focus on Customer Satisfaction to Drive Data Mining Software Market Growth

In today’s hyper-competitive and digitally connected marketplace, customer satisfaction has emerged as a critical factor for business sustainability and growth. The growing focus on enhancing customer satisfaction is proving to be a significant driver in the expansion of the data mining software market. Organizations are increasingly leveraging data mining tools to sift through vast volumes of customer data—ranging from transactional records and website activity to social media engagement and call center logs—to uncover insights that directly influence customer experience strategies. Data mining software empowers companies to analyze customer behavior patterns, identify dissatisfaction triggers, and predict future preferences. Through techniques such as classification, clustering, and association rule mining, businesses can break down large datasets to understand what customers want, what they are likely to purchase next, and how they feel about the brand. These insights not only help in refining customer service but also in shaping product development, pricing strategies, and promotional campaigns. For instance, Netflix uses data mining to recommend personalized content by analyzing a user's viewing history, ratings, and preferences. This has led to increased user engagement and retention, highlighting how a deep understanding of customer preferences—made possible through data mining—can translate into competitive advantage. Moreover, companies are increasingly using these tools to create highly targeted and customer-specific marketing campaigns. By mining data from e-commerce transactions, browsing behavior, and demographic profiles, brands can tailor their offerings and communications to suit individual customer segments. For Instance Amazon continuously mines customer purchasing and browsing data to deliver personalized product recommendations, tailored promotions, and timely follow-ups. This not only enhances customer satisfaction but also significantly boosts conversion rates and average order value. According to a report by McKinsey, personalization can deliver five to eight times the ROI on marketing spend and lift sales by 10% or more—a powerful incentive for companies to adopt data mining software as part of their customer experience toolkit. (Source: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/personalizing-at-scale#/) The utility of data mining tools extends beyond e-commerce and streaming platforms. In the banking and financial services industry, for example, institutions use data mining to analyze customer feedback, call center transcripts, and usage data to detect pain points and improve service delivery. Bank of America, for instance, utilizes data mining and predictive analytics to monitor customer interactions and provide proactive service suggestions or fraud alerts, significantly improving user satisfaction and trust. (Source: https://futuredigitalfinance.wbresearch.com/blog/bank-of-americas-erica-client-interactions-future-ai-in-banking) Similarly, telecom companies like Vodafone use data mining to understand customer churn behavior and implement retention strategies based on insights drawn from service usage patterns and complaint histories. In addition to p...

Over the past five hundred years, North Americans have increasingly turned to mining to produce many of their basic social and cultural objects. From cell phones to cars and roadways, metal pots to...

This line chart displays news by publication date using the aggregation count. The data is filtered where the keywords includes Gold mines and mining-History.

From the site: "Coverages containing Anthracite coal seam data by quadrangle for the state of Pennsylvania. Digitized from the Harrisburg Bureau of Mining and Reclamation mylar map system each quadrangle contains multiple coverages identifying seams in that quad. Also includes coal mining refuse disposal sites, permitted sites, point coverages of deep mine entry and other surface features of deep mines and Small Operators Assistance Program (SOAP) areas."

This dataset is about news. It has 29 rows and is filtered where the keywords includes Gold mines and mining-History. It features 10 columns including source, publication date, section, and news link.

Data associated with paper: "Community detection with metadata in a network of influence between painters"

The ASIAS effort builds on demonstrations that an open exchange of information contributes to improved aviation safety. ASIAS is a comprehensive effort, covering the collection and secure maintenance of aviation data, the analysis performed on that data, and long-term research to better extract safety information from the data. In the mid-90s, NASA researchers started briefing the JIMDAT of CAST on how extracting and integrating information from many sources, and multiple perspectives (including controllers and flight crews) could help them improve aviation safety. The NASA Integrated Safety Data for Strategic Response (ISDSR) concept was incorporated into the JIMDAT concept, ASIAS, which was presented to CAST as essential capabilities, and was then adopted. In parallel with these activities, the FAA encouraged NASA to undertake the Information Sharing Initiative (ISI), a collaborative effort among FAA, NASA, the air carriers and the unions, to develop the DNFA and DNAA (two key srouces of data for ASIAS). A 5-yr plan for collaboration between NASA and the FAA to develop ISDSR was proposed, but was never put into place. That plan would have continued the collaboration with provisions for NAS to develop the analytical tools and transfer them to the FAA for implementation. NASA has, and continues to, develop advanced algorithms to mine the various data sources for information that could continue to maintain and improve the safety of the air transportation system. Such algorithms have already been developed by NASA to identify atypical flights revealing unexpected events and etermine why they were anomalous, to identify anomalous cockpit procedures (switches flipped in the cockpit) during takeoff and landing for possible evidence of problems with the automated systems, and to categorize submitted safety reports such as those submitted to ASRS or ASAP into one or more defined categories to aid the search for clues as to why safety-related events may have occurred. ASIAS provides a vital mechanism for monitoring for safety concerns as we transition to the Next Generation Air Transportation System (NextGen). Not only can ASIAS examine for any indication of hypothesized concerns, but, with the NASA-developed data mining tools, ASIAS can also monitor for statistical trends suggesting the potential emergence of new issues unanticipated or unimagined during the design and testing of NextGen concepts. ASIAS has been carefully developed to capitalize upon the best attributes of earlier research at NASA, while also providing necessary guarantees for anonymity and data protection and while using scientifically justified, rigorous methods for estimating frequencies and causality. NASA's role in the ASIAS effort is to continue to develop these advanced data mining tools and methods to better analyze data voluntarily provided by the aviation community. Acronym List: ASAP: Aviation Safety Action Program ASRS: Aviation Safety Reporting System ASIAS: Aviation Safety Information Analysis & Sharing CAST: Commercial Aviation Safety Team FAA: Federal Aviation Administration ISDSR: Integrated Safety Data for Strategic Response ISI: Information Sharing Initiative JIMDAT: Joint Implementation Monitoring Data Analysis Team NASA: National Aeronautics and Space Administration

This repository contains supplementary materials for the following conference paper:

V. Švábenský, R. Weiss, J. Cook, J. Vykopal, P. Čeleda, J. Mache, R. Chudovský, A. Chattopadhyay.
Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises.
In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE '22), 2022.
DOI: https://doi.org/10.1145/3478431.3499414

The materials include the research dataset, source code, and graphs. For more information about the materials, please see the readme in the attached ZIP file.

If you use or build upon the materials, please use the BibTeX entry below to cite the original work.

@inproceedings{Svabensky2022evaluating,
  author  = {\v{S}v\'{a}bensk\'{y}, Valdemar and Weiss, Richard and Cook, Jack and Vykopal, Jan and \v{C}eleda, Pavel and Mache, Jens and Chudovsk\'{y}, Radoslav and Chattopadhyay, Ankur},
  title   = {{Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises}},
  booktitle = {Proceedings of the 53rd ACM Technical Symposium on Computer Science Education},
  series  = {SIGCSE 2022},
  location = {Providence, RI, USA},
  year   = {2022},
  publisher = {Association for Computing Machinery},
  address  = {New York, NY, USA},
  numpages = {7},  
  isbn   = {978-1-4503-9070-5},
  doi    = {10.1145/3478431.3499414},
}

Material and analyses on sites of historic silver mining and research papers of the DFG research projects Mining and metallurgy in Southwest China, 14th to 19th centuries” and “Environments, people and mining in the Far Southwest of China since 1500: Cross-disciplinary explorations” / Dokumentationen historischer Bergbaustandorte und Papers aus den DFG-Projekten "Bergbau und Metallurgie in Südwestchina, 14. bis 19 Jahrhundert: Archäo-metallurgische und historisch geographische Untersuchungen" und Umwelt, Gesellschaft und Bergbau im fernen Südwestens Chinas seit 1500”

Enron is a well-known dataset in network science and text mining. It has been widely studied in academia. In network science, several different static networks appear in the literature. However, up to now, no dynamic network has been published, even though the email conversations have timestamps.We processed the original dataset to extract a dynamic network, In original dataset, It contains 158 nodes representing Enron employees between 1997 and 2002. All the addresses in the From and To fields of each email are considered, resulting in a network of 28802 nodes representing a distinct email addresses. A time span of one month is chosen for the time slices, generating 46 time slices. Two nodes are connected if the corresponding persons emailed each other during the given time slice. We did not make any distinction between sender and receiver, and thus produced an undirected dynamic network.

Evolution Mining reported AUD802.33M in Operating Profit for its fiscal semester ending in June of 2025. Data for Evolution Mining | EVN - Operating Profit including historical, tables and charts were last updated by Trading Economics this last December in 2025.

This is a point coverage containing 12 years (1984 through 1995) of coal mining history in the Colorado Plateau coal assessment study area. This layer was derived from the Mine Safety and Health Administration's (MSHA) Information Resource Center data for the years 1984 through 1995.

Leveraging Data Mining, Active Learning, and Domain Adaptation for Efficient Discovery of Advanced Oxygen Evolution Electrocatalysts

This repository supports our Science Advances paper, “Leveraging Data Mining, Active Learning, and Domain Adaptation for Efficient Discovery of Advanced Oxygen Evolution Electrocatalysts,” (previous preprint on arXiv https://arxiv.org/abs/2407.04877) and serves as a roadmap to our data and code. The repository is organized into two main parts: Experimental Records, Raw Data, and Figures (released under CC0), Supplementay Notes (CC-BY) and the Machine Learning Scripts (released under the MIT License). All materials are available on Dryad (DOI: 10.5061/dryad.nk98sf83g) and GitHub (https://github.com/ruiding-uchicago/DASH).

Experimental Records, Raw Data, and Supplementary Figures

Overview: This repository contains experimental data—including raw electrochemica...

This dataset proposes lists of multi-terms which are characteristic to domains of sciences (ex. 2000 terms about common in embryology research) and the co-occurrence of its items in the literature through time. Each datasets contains: - a list of terms (or N-grams), one term per line - a spare matrix description of their co-occurrence. On each line we have a 4-plet (period, term1Id, term2Id, number of co-occurrences). For a given period (eg. 2001) and two terms X and Y, the number of co-occurrences is the number of times X and Y appear altogether in an abstract or a title of a reference corpora. In this study, the two corpora are the Thomson ISI Web of Science and MedLine. The name of the corpora is given in the filename.

For research, History mining: the use of computing to study the writing of history in the Southern Cone (1922-1940), we are building a database with magazines, books and publications in newspapers capable of boosting research in the humanities. The table shows the metadata of the research obtained through searches in the database so far. The data deal with intellectual networks, organization of congresses, connection connections, crossings and word frequencies.

Mine Shaft Locations were collected by a variety of methods from 1869 in some areas of the state. Mainly concentrating in Ballarat and Bendigo. In places a shaft may be recorded multiple times with a different source.
In cases where several shaft locations are shown close together (generally with separations less than stated position errors) and they have different sources, it is possible that one shaft has been mapped several times. In cases where several shaft locations are shown close together but they have the same information source, it is possible that each shaft location represents a different shaft on the ground.
Bendigo Mines Hazard Data - a Review and History

The above created a dataset of around 19000 records

More recently, information has been compiled from data from
David Bannear - 2017
DELWP - 2017
Parks Vic - 2017
Peter Ward - 2019 - Public Contributor for the Victorian High Country
Fred Sargent - 2019 to 2022 - Public Contributor for the Victorian High Country (many thousands added)
Peter Quinn - 2019 to 2022 - Public contributor
Source for these is marked as Private survey - and source comment contains details.

Data is largely shafts. Sometimes quarries, open stopes, adits or workings.

Another useful dataset is MINSITE aka Vicmine aka Mines and Mineral Occurrences (more for resource, production, reference links and mineplans)