Data Mining Algorithms and Applications
194 papers on this topic
Movement Control of Smart Mosque's Domes using CSRNet and Fuzzy Logic Techniques
Mosques are worship places of Allah and must be preserved clean, immaculate, provide all the comforts of the worshippers in them. The prophet's mosque in Medina/ Saudi Arabia is one of the most...
Large Language Models are Effective Priors for Causal Graph Discovery
Causal structure discovery from observations can be improved by integrating background knowledge provided by an expert to reduce the hypothesis space. Recently, Large Language Models (LLMs) have...
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models
Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited. In this work, we demonstrate how LLMs can use their compressed world...
EBES: Easy Benchmarking for Event Sequences
Event Sequences (EvS) refer to sequential data characterized by irregular sampling intervals and a mix of categorical and numerical features. Accurate classification of these sequences is crucial...
Generating the Traces You Need: A Conditional Generative Model for Process Mining Data
In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing the features of...
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
Pawlak rough set and neighborhood rough set are the two most common rough set theoretical models. Pawlak can use equivalence classes to represent knowledge, but it cannot process continuous data;...
Relational Causal Discovery with Latent Confounders
Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown. While several causal discovery algorithms exist...
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction
Representation (feature) space is an environment where data points are vectorized, distances are computed, patterns are characterized, and geometric structures are embedded. Extracting a good...
An improved column-generation-based matheuristic for learning classification trees
Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision trees are fast but generate suboptimal...
Case Studies of Causal Discovery from IT Monitoring Time Series
Information technology (IT) systems are vital for modern businesses, handling data storage, communication, and process automation. Monitoring these systems is crucial for their proper functioning...
Scalable Rule-Based Representation Learning for Interpretable Classification
Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based...
Eliminating The Impossible, Whatever Remains Must Be True
The rise of AI methods to make predictions and decisions has led to a pressing need for more explainable artificial intelligence (XAI) methods. One common approach for XAI is to produce a post-hoc...
An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Prediction
Suffix prediction of business processes forecasts the remaining sequence of events until process completion. Current approaches focus on predicting the most likely suffix, representing a single...
Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision
The widespread adoption of mobile devices and data collection technologies has led to an exponential increase in trajectory data, presenting significant challenges in spatio-temporal data mining,...
Motiflets -- Simple and Accurate Detection of Motifs in Time Series
A time series motif intuitively is a short time series that repeats itself approximately the same within a larger time series. Such motifs often represent concealed structures, such as heart beats...
CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analytics
Predictive process analytics focuses on predicting future states, such as the outcome of running process instances. These techniques often use machine learning models or deep learning models (such...
Unbiased Gradient Boosting Decision Tree with Unbiased Feature Importance
Gradient Boosting Decision Tree (GBDT) has achieved remarkable success in a wide variety of applications. The split finding algorithm, which determines the tree construction process, is one of the...
Clustering of timed sequences -- Application to the analysis of care pathways
Improving the future of healthcare starts by better understanding the current actual practices in hospital settings. This motivates the objective of discovering typical care pathways from patient...