A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper explores how mining costs, rewards, and blockchain security are interconnected.
Study predicts shear stress in compound channels using data mining and machine learning.
This paper proposes a communication-efficient distributed algorithm for high-dimensional data mining.
New method samples triplets from data distributions for training Triplet networks.
ATOM improves robust OOD detection by mining informative auxiliary examples.
In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into acc…
CURIE uses cellular automata to detect concept drift in data streams.
Study shows Bitcoin security tied to mining rewards and prices.
Distributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data classification framework where data is gathered by distributed data sources and…
Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining. However, heterogeneous EHR data types and biased ascertainment impose computational challenges. Here, we present mixEHR, an unsupervised g…
The paper analyzes market risk factors for a mining company using a VAR model with stable distribution.
S2M optimizes mining for diverse data subpopulations.
Model shows how centralization occurs in cryptocurrency mining.
Data mining is about obtaining new knowledge from existing datasets. However, the data in the existing datasets can be scattered, noisy, and even incomplete. Although lots of effort is spent on developing or fine-tuning data mining models to make them more robust to the noise of the input data, their qualities still st…
This paper tracks coin circulation in Bitcoin to identify miners and analyze mining pool structures.
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…
QuantaAlpha uses evolutionary algorithms to mine financial alpha robustly across market distributions.
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…
This paper categorizes and analyzes existing outlying aspect mining methods.
Paper detects common subtrees with identical labels in trees.
Smartphones have become the ultimate 'personal' computer, yet despite this, general-purpose data-mining and knowledge discovery tools for mobile devices are surprisingly rare. DataLearner is a new data-mining application designed specifically for Android devices that imports the Weka data-mining engine and augments it …
Paper proposes a new REINFORCE algorithm for mining formulaic alpha factors with reduced variance.
MCRapper efficiently computes patterns in data using Monte-Carlo Rademacher Averages.
Safe Pattern Pruning reduces pattern explosion in predictive pattern mining.
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…
Peer-reviewed research and mined data predict stock returns similarly.
Proposes MLDP for modeling multilinear data.
This paper introduces C-DSL to improve data mining outcomes by considering context.
IFGAN uses feature-specific GANs for missing value imputation.
Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high-quality solutions can lead to an efficient exploration of the search space, along with a si…
Confidence intervals improve evaluation of binary prediction rules in data mining.
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…
With the growing interest on Network Analysis, Relational Data Mining is becoming an emphasized domain of Data Mining. This paper addresses the problem of extracting representative elements from a relational dataset. After defining the notion of degree of representativeness, computed using the Borda aggregation procedu…
The paper uses GIS data to predict urban sprawl.
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the selection bias, i.e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases. In …
Paper presents a framework to automatically discover constraints from data.
Lack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews. To alleviate this problem, we first propose an algorithm to automatically mine extraction rules from existing training examples based on dependency parsing results. The mined rules are the…
Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications. In order to build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules…
We present a novel algorithm, Westfall-Young light, for detecting patterns, such as itemsets and subgraphs, which are statistically significantly enriched in one of two classes. Our method corrects rigorously for multiple hypothesis testing and correlations between patterns through the Westfall-Young permutation proced…
This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its strengths and unique features. The demonstrated applications provi…
Operating in a dynamic real world environment requires a forward thinking and adversarial aware design for classifiers, beyond fitting the model to the training data. In such scenarios, it is necessary to make classifiers - a) harder to evade, b) easier to detect changes in the data distribution over time, and c) be ab…
We propose a new class of metrics on sets, vectors, and functions that can be used in various stages of data mining, including exploratory data analysis, learning, and result interpretation. These new distance functions unify and generalize some of the popular metrics, such as the Jaccard and bag distances on sets, Man…
Randomization helps verify if data mining results are due to inherent patterns.
Paper presents a new method for privacy-preserving GLMs on vertically partitioned data.
This paper applies secure multi-party computation to K-means clustering to protect private data.
The process of exploring and exploiting Oil and Gas (O&G) generates a lot of data that can bring more efficiency to the industry. The opportunities for using data mining techniques in the "digital oil-field" remain largely unexplored or uncharted. With the high rate of data expansion, companies are scrambling to develo…
Method extracts taint flows to classify Bitcoin mining pools.
A new framework for mining high utility patterns in interval-based sequences.