Papers Sequential Pattern Mining
“Sequential Pattern Mining” 태그가 달린 논문 44편 · 필터 해제
AgentOS: From Application Silos to a Natural Language-Driven Data Ecosystem
The rapid emergence of open-source, locally hosted intelligent agents marks a critical inflection point in human-computer interaction. Systems such as OpenClaw demonstrate that Large Language Model (LLM)-based agents can…
Sequential Pattern MiningKnowledge GraphsFrequent Pattern Mining approach to Image Compression
The paper focuses on Image Compression, explaining efficient approaches based on Frequent Pattern Mining(FPM). The proposed compression mechanism is based on clustering similar pixels in the image and thus using cluster …
Sequential Pattern MiningImage CompressionExamining Student Interactions with a Pedagogical AI-Assistant for Essay Writing and their Impact on Students Writing Quality
The dynamic nature of interactions between students and GenAI, as well as their relationship to writing quality, remains underexplored. While most research has examined how general-purpose GenAI can support writing, fewe…
Sequential Pattern MiningUncovering Students' Inquiry Patterns in GenAI-Supported Clinical Practice: An Integration of Epistemic Network Analysis and Sequential Pattern Mining
Assessment of medication history-taking has traditionally relied on human observation, limiting scalability and detailed performance data. While Generative AI (GenAI) platforms enable extensive data collection and learni…
Sequential Pattern MiningTemporal SequencesFunctional Groups are All you Need for Chemically Interpretable Molecular Property Prediction
Molecular property prediction using deep learning (DL) models has accelerated drug and materials discovery, but the resulting DL models often lack interpretability, hindering their adoption by chemists. This work propose…
Molecular Property PredictionSequential Pattern MiningA Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences
In rapidly evolving e-commerce industry, the capability of selecting high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) mode…
Active LearningSequential Pattern MiningThe Crowd in MOOCs: A Study of Learning Patterns at Scale
The increasing availability of learning activity data in Massive Open Online Courses (MOOCs) enables us to conduct a large-scale analysis of learners' learning behavior. In this paper, we analyze a dataset of 351 million…
Sequential Pattern MiningCausal Analysis of Customer Churn Using Deep Learning
Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollar-value: g…
Deep LearningMarketingSequential Pattern MiningMining compact high utility sequential patterns
High utility sequential pattern mining (HUSPM) aims to mine all patterns that yield a high utility (profit) in a sequence dataset. HUSPM is useful for several applications such as market basket analysis, marketing, and w…
MarketingSequential Pattern MiningVocal Bursts Intensity PredictionSequential pattern mining in educational data: The application context, potential, strengths, and limitations
Increasingly, researchers have suggested the benefits of temporal analysis to improve our understanding of the learning process. Sequential pattern mining (SPM), as a pattern recognition technique, has the potential to r…
Recommendation SystemsSequential Pattern MiningHUSP-SP: Faster Utility Mining on Sequence Data
High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM pro…
Sequential Pattern MiningTowards Sequence Utility Maximization under Utility Occupancy Measure
The discovery of utility-driven patterns is a useful and difficult research topic. It can extract significant and interesting information from specific and varied databases, increasing the value of the services provided.…
Sequential Pattern MiningTowards Correlated Sequential Rules
The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or useful sequential patterns in a large number of sequences. However, simply being aware of utility-eligible patterns is i…
Product RecommendationSequential Pattern MiningTotally-ordered Sequential Rules for Utility Maximization
High utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure…
Sequential Pattern MiningLeveraging Language Foundation Models for Human Mobility Forecasting
In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks. For example, in the task of predicting Place-of-I…
DecoderSequential Pattern MiningTemporal SequencesTaSPM: Targeted Sequential Pattern Mining
Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies …
Sequential Pattern MiningMemory-Efficient Sequential Pattern Mining with Hybrid Tries
This paper develops a memory-efficient approach for Sequential Pattern Mining (SPM), a fundamental topic in knowledge discovery that faces a well-known memory bottleneck for large data sets. Our methodology involves a no…
Sequential Pattern MiningIncremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on…
Sequential Pattern MiningOPP-Miner: Order-preserving sequential pattern mining
A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns…
Sequential Pattern MiningTime SeriesTime Series AnalysisUS-Rule: Discovering Utility-driven Sequential Rules
Utility-driven mining is an important task in data science and has many applications in real life. High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. HUSPM aims to discover all sequentia…
Sequential Pattern Mining