ICDM 2025 Name for Papers invitations researchers to discover the innovative of information mining. This yr’s focus emphasizes progressive methodologies and interdisciplinary collaborations, promising vital developments within the discipline. The decision highlights key analysis areas ripe for exploration, urging submissions that tackle crucial gaps and contribute to the evolving panorama of information science.
Submissions are inspired throughout a spread of subjects, from novel algorithms and theoretical frameworks to functions addressing real-world challenges. The organizers significantly welcome interdisciplinary approaches, fostering collaboration between information mining consultants and researchers from different fields. Assembly the submission deadlines is essential for consideration.
ICDM 2025 Name for Papers
The ICDM 2025 Name for Papers invitations researchers and practitioners to submit high-quality, authentic analysis contributions addressing the most recent developments and challenges in information mining and associated fields. The convention emphasizes cutting-edge methodologies and their functions throughout various domains.
Key Themes and Subjects
This yr’s name for papers prioritizes analysis exploring progressive information mining methods, their theoretical foundations, and impactful real-world functions. Particular areas of curiosity embrace however will not be restricted to: large information analytics, deep studying for information mining, explainable AI, equity and ethics in information mining, and the applying of information mining to societal challenges equivalent to local weather change and healthcare.
The convention welcomes submissions addressing each foundational theoretical points and sensible functions throughout varied disciplines.
Important Analysis Areas
The organizers strongly encourage submissions specializing in a number of key analysis areas. These embrace: the event of novel algorithms for high-dimensional information evaluation; the applying of information mining methods to handle challenges in particular domains, equivalent to finance, healthcare, and social networks; analysis on the moral and societal implications of information mining; and the development of methods for dealing with uncertainty and lacking information in giant datasets.
Submissions exploring interdisciplinary approaches are significantly welcome.
Submission Pointers and Deadlines
Authors are requested to arrange their submissions based on the required pointers, making certain adherence to the formatting necessities and size limitations. All submissions will bear a rigorous peer-review course of. The assessment course of will assess the originality, significance, and technical soundness of every contribution. Accepted papers might be printed within the convention proceedings.
Key Dates and Submission Necessities, Icdm 2025 name for papers
| Stage | Date | Requirement | Particulars |
|---|---|---|---|
| Summary Submission | July 15, 2024 | Summary (500 phrases max) | Submit through the web submission system. |
| Full Paper Submission | August 15, 2024 | Full manuscript (8 pages max) | Adhere to the required formatting pointers. |
| Notification of Acceptance | October 15, 2024 | N/A | Authors might be notified through e mail. |
| Digital camera-Prepared Submission | November 15, 2024 | Last manuscript | Submit the ultimate model of your accepted paper. |
Analyzing Analysis Areas: Icdm 2025 Name For Papers
The ICDM 2025 Name for Papers highlights a number of key analysis areas inside information mining and data discovery. Analyzing these areas reveals vital overlaps and distinct focuses, every promising impactful developments in varied fields. This evaluation will examine and distinction these areas, discover their potential affect, assessment the present state-of-the-art, and illustrate progressive methodologies.
Knowledge Mining for Societal Good
This space focuses on leveraging information mining methods to handle urgent societal challenges. Analysis right here emphasizes moral concerns and accountable information utilization. The potential affect is substantial, starting from bettering public well being outcomes to enhancing environmental sustainability and selling social justice. Present state-of-the-art analysis entails growing explainable AI (XAI) strategies for higher transparency and accountability in data-driven decision-making, significantly in delicate areas like legal justice and healthcare.
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Modern analysis methodologies embrace:
- Creating fairness-aware algorithms to mitigate bias in data-driven techniques, for instance, through the use of methods like adversarial debiasing or re-weighting samples.
- Using privacy-preserving information mining methods, equivalent to federated studying or differential privateness, to guard delicate particular person data whereas nonetheless extracting worthwhile insights.
- Creating explainable fashions to reinforce belief and understanding within the outcomes of information mining analyses, for example, by using methods like LIME or SHAP.
Graph Knowledge Mining and Community Evaluation
This space explores the extraction of data from complicated graph-structured information. Its affect spans quite a few domains, together with social community evaluation, organic networks, and advice techniques. The present state-of-the-art entails developments in graph neural networks (GNNs) for node classification, hyperlink prediction, and group detection. Scalability and dealing with of dynamic graphs stay vital challenges.
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Examples of progressive methodologies:
- Creating novel GNN architectures optimized for particular graph varieties, equivalent to heterogeneous graphs or temporal graphs.
- Using graph embedding methods to symbolize graph information in lower-dimensional vector areas, facilitating environment friendly processing and evaluation.
- Using graph mining algorithms to detect anomalies and patterns in large-scale networks, aiding in fraud detection or illness outbreak prediction.
Deep Studying for Knowledge Mining
This space focuses on making use of deep studying methods to numerous information mining duties. The potential affect is widespread, with functions in picture recognition, pure language processing, and time sequence forecasting. The present state-of-the-art consists of developments in convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Challenges embrace mannequin interpretability and the necessity for giant labeled datasets.
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Modern methodologies contain:
- Creating novel deep studying architectures tailor-made to particular information mining duties, equivalent to autoencoders for anomaly detection or generative adversarial networks (GANs) for information augmentation.
- Using switch studying to leverage pre-trained fashions and cut back the necessity for giant labeled datasets, significantly useful in resource-constrained eventualities.
- Implementing methods for mannequin compression and environment friendly inference to deploy deep studying fashions on resource-limited gadgets.
Figuring out Potential Analysis Gaps
The ICDM 2025 Name for Papers highlights a number of key areas inside information mining, however a cautious evaluation reveals vital alternatives for novel analysis. Specializing in these gaps permits researchers to contribute meaningfully to the development of the sector, pushing the boundaries of what is potential with information evaluation and interpretation. By figuring out these gaps and proposing progressive options, the ICDM 2025 convention can function a catalyst for future breakthroughs.Present analysis closely emphasizes particular methods, typically overlooking the broader contextual implications and limitations.
This creates a number of promising avenues for future analysis. As an example, the rising complexity of information necessitates extra sturdy strategies for dealing with uncertainty and noise, whereas the moral concerns surrounding information privateness and bias stay inadequately addressed in lots of present algorithms. Addressing these limitations will considerably improve the reliability and trustworthiness of information mining outcomes.
Explainable AI (XAI) in Excessive-Dimensional Knowledge
The applying of Explainable AI (XAI) methods to high-dimensional datasets presents a big problem. Present XAI strategies typically wrestle to supply clear and concise explanations for predictions made on datasets with quite a few options. This limits the usability and trustworthiness of AI fashions in crucial functions equivalent to medical analysis or monetary threat evaluation. Future analysis ought to give attention to growing novel XAI methods that may successfully deal with the complexities of high-dimensional information whereas sustaining interpretability.
This might contain exploring dimensionality discount methods that protect essential data for rationalization, or growing new rationalization strategies that concentrate on summarizing the important thing elements influencing predictions relatively than detailing the contribution of each single function.
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Robustness and Equity in Federated Studying
Federated studying, which trains fashions on decentralized information with out straight sharing it, presents vital privateness benefits. Nonetheless, present federated studying algorithms are weak to numerous assaults, together with information poisoning and mannequin poisoning. Moreover, biases current in particular person datasets can combination and amplify within the last international mannequin, resulting in unfair outcomes. Analysis is required to develop sturdy and honest federated studying algorithms which might be resilient to assaults and mitigate bias amplification.
This might contain incorporating methods from sturdy statistics and fairness-aware machine studying into the federated studying framework. For instance, a novel strategy may contain incorporating differential privateness mechanisms to guard particular person information contributions whereas concurrently using adversarial coaching to reinforce robustness towards malicious assaults.
Analysis Proposal: A Novel Method to Sturdy Federated Studying
This analysis proposes a novel federated studying algorithm incorporating a strong aggregation mechanism and a fairness-aware mannequin choice course of. The proposed algorithm will tackle the robustness and equity challenges in federated studying by:
- Using a strong aggregation method, equivalent to trimmed imply or median, to scale back the affect of outlier information factors contributed by malicious or biased purchasers. This may improve the robustness of the worldwide mannequin towards information poisoning assaults.
- Integrating a fairness-aware mannequin choice course of that evaluates candidate fashions primarily based on each their efficiency and their equity throughout completely different subgroups throughout the information. This may mitigate the amplification of bias through the mannequin coaching course of.
- Utilizing a multi-agent reinforcement studying framework to dynamically modify the weighting of particular person consumer updates through the aggregation course of, additional enhancing the robustness and equity of the algorithm. This permits the system to be taught optimum aggregation methods over time.
The anticipated final result is a federated studying algorithm that’s extra sturdy to adversarial assaults and produces fairer and extra equitable predictions in comparison with present strategies. This may considerably improve the trustworthiness and applicability of federated studying in delicate functions.
Exploring Interdisciplinary Connections
The ICDM 2025 name for papers highlights quite a few analysis areas ripe for interdisciplinary collaboration. By bringing collectively various views and methodologies, researchers can obtain breakthroughs that may be unattainable inside a single self-discipline. This synergistic strategy fosters innovation and results in extra complete and impactful options to complicated information mining challenges.The potential for cross-disciplinary synergy is substantial. As an example, developments in areas like graph neural networks may enormously profit from collaborations with researchers in social community evaluation, permitting for extra nuanced and correct modeling of complicated social interactions.
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Equally, analysis in explainable AI (XAI) might be considerably enhanced by incorporating data from cognitive science and human-computer interplay to design simpler and reliable AI techniques. Moreover, developments in information privateness and safety can leverage experience from cryptography and legislation to create sturdy and ethically sound information mining options.
Interdisciplinary Workforce for Addressing Knowledge Bias in Algorithmic Determination-Making
Addressing information bias in algorithmic decision-making requires a multi-faceted strategy. A hypothetical interdisciplinary workforce may encompass the next members:
- Knowledge Scientist (Experience: Machine Studying, Knowledge Mining): Answerable for figuring out and quantifying bias in datasets and growing bias mitigation methods.
- Social Scientist (Experience: Sociology, Demography): Supplies context for understanding the social and historic elements that contribute to information bias, making certain the moral implications are thought-about.
- Pc Ethicist (Experience: Ethics, Philosophy of Know-how): Evaluates the moral implications of algorithmic selections and advocates for accountable AI improvement.
- Authorized Professional (Experience: Knowledge Privateness Regulation, Algorithmic Accountability): Ensures compliance with related legal guidelines and rules and advises on authorized points of algorithmic equity.
This collaborative strategy would leverage the strengths of every self-discipline to create simpler bias mitigation methods. The information scientist would use their technical expertise to establish and measure bias, whereas the social scientist would supply worthwhile insights into the societal context of the bias. The pc ethicist would offer a framework for accountable AI improvement, and the authorized skilled would guarantee compliance with related legal guidelines and rules.
This built-in strategy would result in options that aren’t solely technically sound but additionally ethically accountable and legally compliant. For instance, the workforce may develop a novel algorithm that weights completely different information factors in a different way primarily based on their potential for bias, thereby minimizing the affect of skewed information on the ultimate determination. They might additionally develop instruments for auditing algorithms for bias, permitting for steady monitoring and enchancment.
Visualizing Potential Analysis Contributions
Efficient visualization is essential for speaking the affect and key findings of analysis, significantly throughout the complicated discipline of information mining. Visible representations could make summary ideas extra accessible and fascinating for each specialists and a broader viewers, fostering higher understanding and collaboration. This part particulars how visualizations can successfully convey the potential affect of analysis inside a particular space, and spotlight key findings from a hypothetical research.
Visualizing the Affect of Analysis on Anomaly Detection in Monetary Transactions
A compelling approach to illustrate the anticipated affect of improved anomaly detection in monetary transactions can be a stacked bar chart. The chart’s X-axis would symbolize completely different time intervals (e.g., quarters of a yr). The Y-axis would symbolize the financial worth of fraudulent transactions. Every bar can be segmented into three sections: “Detected and Prevented” (representing profitable anomaly detection), “Detected however Not Prevented” (representing circumstances the place detection occurred however prevention failed on account of exterior elements), and “Undetected” (representing fraudulent transactions missed by the system).
The chart would examine these values for a baseline system (representing present expertise) and a proposed improved system (integrated with the analysis findings). A visually clear discount within the “Undetected” section and a rise within the “Detected and Prevented” section for the improved system would powerfully show the optimistic affect of the analysis. The chart would additionally embrace a legend clearly explaining every section and the entire financial worth prevented or misplaced.
For instance, a discount of undetected fraud from $10 million to $2 million over a yr can be clearly illustrated, demonstrating the numerous monetary advantages of the proposed analysis.
Infographic Highlighting Key Findings of a Hypothetical Research
This infographic would give attention to a hypothetical research addressing the analysis hole in understanding the affect of social media sentiment on inventory market fluctuations. The infographic can be divided into three principal sections. The primary part would current a concise abstract of the analysis query and methodology utilizing clear, concise language and probably a easy flowchart illustrating the info processing pipeline.
The second part would current the important thing findings by way of a mixture of visuals. A scatter plot would illustrate the correlation between social media sentiment (optimistic, destructive, impartial) and every day inventory worth adjustments, displaying a statistically vital relationship. A pie chart would then break down the proportion of worth fluctuations attributable to completely different sentiment classes. The third part would current the implications of the findings.
This might embrace a concise bullet-point checklist summarizing the sensible functions of the analysis, equivalent to improved algorithmic buying and selling methods or extra correct market prediction fashions. The infographic would use a visually interesting colour scheme, clear font selections, and minimal textual content to make sure ease of understanding and retention. The general design would preserve knowledgeable but participating type, appropriate for a broad viewers, together with buyers, policymakers, and researchers.
Enhancing Communication of Analysis Findings by way of Visualization
Visualizations considerably improve the communication of analysis findings to a broader viewers by reworking complicated information into simply digestible codecs. Charts, graphs, and infographics cater to completely different studying kinds, making analysis extra accessible to non-specialists. For instance, a posh statistical mannequin might be simplified by way of a visible illustration of its key parameters and their interactions, facilitating a faster and extra intuitive understanding.
Furthermore, compelling visualizations can enhance viewers engagement, making the analysis extra memorable and impactful. Through the use of visuals, researchers can successfully talk the importance and implications of their work, resulting in wider adoption and affect. The usage of applicable visuals permits researchers to transcend presenting simply outcomes and to speak the story behind the analysis, its context, and its potential affect.
Assessing the Significance of ICDM 2025
ICDM 2025 holds vital significance for the info mining group as a premier venue for presenting cutting-edge analysis and fostering collaboration amongst main researchers and practitioners. Its affect extends past the rapid convention, shaping the long run trajectory of the sector by way of the dissemination of novel methodologies, algorithms, and functions.The decision for papers for ICDM 2025 displays the present tendencies and future instructions in information mining by emphasizing areas equivalent to explainable AI, equity and accountability in algorithms, the moral concerns of huge language fashions, and the applying of information mining methods to rising domains like local weather science and personalised medication.
This give attention to each methodological developments and impactful functions underscores the rising maturity and societal relevance of the sector.
ICDM 2025’s Anticipated Contributions to Knowledge Mining
Accepted papers at ICDM 2025 are anticipated to contribute considerably to the development of information mining in a number of methods. These contributions will vary from the event of novel algorithms and theoretical frameworks to the demonstration of sensible functions that tackle real-world challenges. The rigorous peer-review course of ensures a excessive normal of high quality, making the accepted papers worthwhile assets for researchers and practitioners alike.
Instance Presentation Construction: Hypothetical Accepted Paper
The next construction Artikels a possible presentation summarizing the important thing findings of a hypothetical accepted paper specializing in a novel algorithm for anomaly detection in time-series information from good grids.
- Introduction: Briefly introduce the issue of anomaly detection in good grids and its significance. Spotlight the constraints of present strategies.
- Proposed Methodology: Element the novel algorithm, together with its underlying rules, mathematical formulation, and implementation particulars. Emphasize its novelty and benefits over present methods. A visible illustration of the algorithm’s workflow could possibly be included, maybe a flowchart displaying the completely different steps concerned in processing information and figuring out anomalies.
- Experimental Outcomes: Current the outcomes of experiments carried out on real-world good grid information. Embody quantitative metrics equivalent to precision, recall, and F1-score to show the algorithm’s efficiency. Evaluate the outcomes to these obtained utilizing state-of-the-art strategies. Visualizations equivalent to ROC curves and precision-recall curves could possibly be used for example the efficiency successfully. As an example, a graph displaying the F1-score of the brand new algorithm in comparison with three present strategies could possibly be offered.
- Dialogue and Conclusion: Talk about the implications of the findings and spotlight the algorithm’s potential affect on good grid administration. Deal with any limitations of the research and counsel instructions for future analysis. For instance, the presentation may point out the algorithm’s scalability and potential challenges in dealing with very giant datasets. It may additionally focus on future work on extending the algorithm to deal with several types of anomalies or integrating it into a bigger good grid monitoring system.
