SDSM extracts statistically significant sub-trajectories from large trajectory datasets.
problem Discerning moving patterns that are more characteristic of one group of trajectories than another.
method Statistically Discriminative Sub-trajectory Mining (SDSM) method using tree representation and permutation-based statistical inference.
result SDSM efficiently extracts statistically significant sub-trajectories from massive trajectory datasets.
Predicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism…
WS-II algorithm segments trajectories with high accuracy.
problem Trajectory segmentation for diverse data sources.
method Supervised learning with sliding window analysis.
result WS-II outperforms other methods in all datasets.
QuantaAlpha uses evolutionary algorithms to mine financial alpha robustly across market distributions.
problem Challenges in alpha mining due to market noise and regime shifts.
method Evolutionary framework treating each mining run as a trajectory, mutation, crossover, targeted revision, and reuse of effective patterns.
result Consistent gains over strong baselines and prior systems, achieving high IC and ARR.
TLRS improves predictive power of mined formulaic alpha factors.
problem Sparse rewards in RL for mining formulaic alpha factors.
method Trajectory-level Reward Shaping (TLRS) with reward centering.
result TLRS boosts predictive power by 9.29% over existing methods.
Pattern ensembling fills in missing or inaccurate trajectory data.
problem Incompleteness, missing information, and inaccuracies in geolocation data.
method Probabilistically ensemble similar trajectory patterns from the vicinity.
result Reconstructs missing or unreliable trajectory segments effectively.
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajec…
Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes spatiotemporal characteristics. It is a crucial technique to connect feature-based data mining with trajectory data. Trajectory representati…
Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may b…
Paper infers human mobility from sparse trajectories.
problem Modeling and inferring human mobility from sparse trajectory data.
method Proposes a single trajectory inference algorithm and a deep learning architecture for multiple trajectories.
result Deep learning model achieves 2x overall accuracy improvement on sparse trajectories.
TraLFM models human mobility patterns from traffic trajectories.
problem Understanding human mobility patterns from traffic data.
method Latent factor modeling of sequential, personal, and temporal factors.
result TraLFM significantly outperforms state-of-the-art methods in latent factor analysis and next location prediction.
The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecast…
This work proposes a new feature for transportation mode classification using GPS trajectories.
problem Classifying transportation modes from GPS trajectories to optimize urban mobility.
method The Ordinal Pattern Transition Graph and its self-transition probability are used for classification.
result The proposed feature outperforms existing methods in transportation mode classification.
Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can save money and time for authorities and the public. It may reduce the fuel consumption and commute time and moreover, may provide more pleasa…
Game-theoretic analysis of mining gaps in blockchain systems.
problem Strategic mining behavior and its impact on blockchain stability.
method Game-theoretic model and Nash equilibrium analysis.
result Mining gaps can destabilize blockchain systems, especially with decreasing block rewards.
Alpha-GPT mines new trading signals with human-AI interaction.
problem Mining new alphas for effective trading signals.
method Human-AI interaction and prompt engineering algorithmic framework.
result Demonstrates Alpha-GPT's effectiveness in generating creative, insightful, and effective alphas.
The paper explores how mining costs, rewards, and blockchain security are interconnected.
problem Understanding the interdependencies between mining costs, mining rewards, and blockchain security.
method Theoretical derivation and empirical analysis using daily crypto market data and autoregressive distributed lag approach.
result Cryptocurrency price and mining rewards are intrinsically linked to blockchain security outcomes.
Data preprocessing improves data quality for robust data mining.
problem Noisy and incomplete data hinders data mining models.
method Overview of data cleaning, transformation, and preprocessing methods.
result Preprocessing significantly affects data mining model performance.
This paper categorizes and analyzes existing outlying aspect mining methods.
problem Finding unique features in data objects that differ from others.
method Grouping and analyzing existing outlying aspect mining approaches in three categories.
result Comparison of strengths, weaknesses, and time complexities of different techniques.
Game theory shows miners' hardware improvements don't centralize mining.
problem Decentralization of cryptocurrency mining.
method Game-theoretical model of mining efficiency and competition.
result Advancements in mining hardware efficiency do not lead to centralization.
DataLearner simplifies data mining on Android devices.
problem Lack of general-purpose data-mining tools for mobile devices.
method Augments Weka engine with Charles Sturt University algorithms, providing 40 mining algorithms.
result Delivers classification accuracy similar to PCs/laptops with acceptable speed and battery life.
Study uses NMF to analyze multimorbidity patterns in large EHR dataset.
problem Understanding and quantifying multimorbidity patterns over time.
method Non-negative Matrix Factorisation (NMF) for temporal phenotyping.
result Temporal characteristics of disease clusters reveal new multimorbidity patterns.
Let M be a smooth manifold and F be a vector field on M. My article ["Smooth shifts along trajectories of flows", Topol. Appl. 130 (2003) 183-204, arXiv:math/0106199] concerning the homotopy types of the group of diffeomorphisms preserving orbits of F contains two errors. They imply that the principal statement…
This paper analyzes the profitability of selfish mining on blockchain, considering the risk of ruin.
problem The profitability of selfish mining on blockchain, considering the risk of ruin.
method Formulated a stochastic model and used tools from applied probability and analysis to determine expected profit.
result Explicit expressions for expected profit under different scenarios were derived, identifying conditions for selfish mining as a strategic advantage.
Agricultural research has been profited by technical advances such as automation, data mining. Today, data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively…
StreaMRAK improves KRR for streaming data.
problem Streaming data with memory constraints.
method Divides problem into levels of resolution, sub-sampling.
result Efficiently integrates new samples, reduces memory and complexity.
This paper tracks coin circulation in Bitcoin to identify miners and analyze mining pool structures.
problem Identifying and understanding Bitcoin miners and their profit distribution schemes.
method Constructs fresh coin circulation networks and uses a heuristic algorithm to compare networks from different mining pools.
result Infers common profit distribution schemes of Bitcoin mining pools and observes an increasing trend in miner numbers.
We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, an…
Study shows Bitcoin mining with surplus electricity can boost KEPCO's financial stability.
problem Improving energy resource efficiency and reducing KEPCO's debt.
method Utilized surplus electricity for Bitcoin mining using Antminer S21 XP Hyd, analyzed with Random Forest Regressor and Long Short-Term Memory models.
result Bitcoin mining with surplus electricity generates economic revenue, minimizes energy loss, and resolves payment issues for KEPCO.
A new framework for mining high utility patterns in interval-based sequences.
problem Mining patterns in events that persist over varying time intervals and considering event utility.
method Integrates utility into interval-based sequences and proposes HUIPMiner algorithm with pruning strategy.
result HUIPMiner efficiently finds high utility patterns in real datasets.
The problem of frequent pattern mining has been studied quite extensively for various types of data, including sets, sequences, and graphs. Somewhat surprisingly, another important type of data, namely rank data, has received very little attention in data mining so far. In this paper, we therefore addresses the problem…
Data mining enhances a heuristic for the Minimum Latency Problem.
problem Finding optimal solutions for the Minimum Latency Problem efficiently.
method Combining GRASP with data mining to find frequent patterns in high-quality solutions.
result Improved solution quality and reduced computational time compared to existing methods.
Safe Pattern Pruning reduces pattern explosion in predictive pattern mining.
problem Exponential growth of patterns in structured data.
method Safe Pattern Pruning (SPP) method.
result Effective model building in practical data analysis.
This document describes an approach to the problem of predicting dangerous seismic events in active coal mines up to 8 hours in advance. It was developed as a part of the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines. The solutions presented consist of ensembles of various pred…
New measure EC assesses node contributions in nonlinear, time-varying systems.
problem Existing node contribution measures assume linear, time-invariant dynamics, failing for complex, real-world systems.
method Defined 'emergent contribution (EC)' as a dynamical leverage measure from Jacobians of differentiable models.
result EC diverges from average controllability under persistent regime switching and sign reversal, identifying limits of local linearization.
In January 3, 2009, Satoshi Nakamoto gave rise to the "Bitcoin Block Chain" creating the first block of the chain hashing on his computers central processing unit (CPU). Since then, the hash calculations to mine Bitcoin have been getting more and more complex, and consequently the mining hardware evolved to adapt to th…
MotiFiesta learns network motifs efficiently.
problem Efficiently identifying network motifs in real-world datasets.
method Formulated as a node labelling task, using machine learning.
result Demonstrated efficient motif discovery on challenging datasets.
Study shows Bitcoin security tied to mining rewards and prices.
problem Understanding Bitcoin security's dependency on market outcomes.
method Used ARDL approach with daily blockchain and Bitcoin data from 2014-2019.
result Bitcoin security outcomes linked to Bitcoin price and mining rewards.
FedSLIM optimizes compact pattern models across distributed databases without sharing raw data.
problem Privacy-preserving federated descriptive analytics for data silos.
method Federated MDL-based framework using SLIM principle.
result FedSLIM variants preserve high-quality compression structure and recover globally informative patterns.
Peer-reviewed research and mined data predict stock returns similarly.
problem Predicting stock returns using research quality.
method Cross-sectional analysis of 29,000 accounting ratios with t-statistics > 2.0.
result Post-sample performance is largely independent of whether the predictor is peer-reviewed or mined.
Proposes SCR-Apriori for efficient mining of SCR-patterns.
problem Mining high-quality `Set of Contrasting Rules'-pattern (SCR-pattern) efficiently.
method Integrates SCR-pattern structure into Apriori algorithm to prune search space.
result Significantly reduces computational cost compared to state-of-the-art.
The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for text, DNA, and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. Surprisingly, however, little progress has been made on addressing this problem for time series subseque…
AlphaEval evaluates alpha mining models efficiently and comprehensively.
problem Lack of systematic evaluation for alpha mining models.
method Unified, parallelizable evaluation framework assessing predictive power, stability, robustness, financial logic, and diversity.
result AlphaEval achieves evaluation consistency comparable to comprehensive backtesting, providing more comprehensive insights and higher efficiency.
Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database. We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, w…
AlphaForge mines and dynamically combines alpha factors for better investment performance.
problem Inconsistency and inflexibility of fixed factor weights in alpha factor mining.
method Generative-predictive neural network for factor generation and dynamic weight adjustment.
result Demonstrated superior performance in formulaic alpha factor mining and portfolio returns.
The purpose of this study is to estimate the production function and examine the structure of production in the mining sector of Iran. Several studies have already been conducted in estimating production functions of various economic sectors; however, less attention has been paid to mining sectors. After examining the …
GCBS improves contrastive learning performance efficiently.
problem Efficiently mining hard negatives for contrastive learning.
method Global Contrastive Batch Sampling (GCBS) approximates batch assignment problem.
result GCBS improves state-of-the-art performance in sentence embedding and code-search tasks.
Automates text mining by learning effective representations from raw text.
problem Limited automation in text mining for non-experts.
method Meta-learning approach to automatically obtain text representations.
result Proposed methodology yields highly effective text classification pipelines.