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.
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48 results for “graph pattern mining”
R-GPM enables efficient graph pattern mining through user-defined relations.
problem Efficient graph pattern mining through user-defined relations.
method Parallel computing framework with MCMC sampling algorithm and optimizations.
result Efficient estimators for graph pattern statistics with up to 3-orders-of-magnitude computational cost reduction.
SGE learns symbolic node representations from relational data.
problem Mining insights from complex, real-world systems.
method SGE uses frequent pattern mining on a node's neighborhood to learn symbolic node representations.
result SGE outperforms shallow node embedding methods on a venue classification task.
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SPP) for a class of predictive pattern mining problems. The SPP method a…
Mining Rank Datacs.LG
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…
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information …
Method extracts taint flows to classify Bitcoin mining pools.
problem Understanding pseudonymous Bitcoin actors and their transactions.
method Taint analysis and graph embedding methods applied to taint flows.
result Taint flows from the same period show high similarity.
Method finds interestingly dense subgroup connections in graphs.
problem Understanding patterns in graph connectivity based on node attributes.
method Information-theoretic definition of interestingness for subgroup connections.
result Identifies pairs of node subgroups with high or low edge density.
Paper detects common subtrees with identical labels in trees.
problem Finding common subtrees with identical label distribution in tree data.
method Developed an algorithm for tree isomorphism and a new compression scheme for trees.
result The method efficiently finds and compresses common subtrees with identical labels.
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.
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.
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.
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…
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…
Temporal Pattern Mining (TPM) is the problem of mining predictive complex temporal patterns from multivariate time series in a supervised setting. We develop a new method called the Fast Temporal Pattern Mining with Extended Vertical Lists. This method utilizes an extension of the Apriori property which requires a more…
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 …
This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing p…
FSR efficiently discovers significant patterns with few resampled datasets.
problem Mining significant patterns in transactional data, especially subgroups.
method FSR uses resampling to bound the supremum deviation of quality statistics, providing rigorous guarantees on false discoveries.
result FSR effectively discovers significant subgroups with a small number of resampled datasets.
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…
A novel method uses blockchain transaction graphs for Bitcoin price prediction.
problem Insufficient effectiveness of manually designed features for Bitcoin price prediction.
method Mining patterns from Bitcoin transactions using k-order transaction graphs and proposing a novel prediction method.
result The proposed method outperforms state-of-the-art Bitcoin price prediction methods.
Significant pattern mining, the problem of finding itemsets that are significantly enriched in one class of objects, is statistically challenging, as the large space of candidate patterns leads to an enormous multiple testing problem. Recently, the concept of testability was proposed as one approach to correct for mult…
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoni…
MCRapper efficiently computes patterns in data using Monte-Carlo Rademacher Averages.
problem Finding statistically significant patterns in data with limited samples.
method Monte-Carlo Empirical Rademacher Averages (MCERA) for poset families.
result MCRapper provides upper bounds to the discrepancy of functions, enabling efficient pattern mining.
Discovers discriminative patterns in two-class datasets.
problem Discovering patterns that occur more frequently in one class than the other.
method Proposes SSDPS algorithm with an original enumeration strategy exploiting anti-monotonicity.
result SSDPS outperforms other algorithms in terms of efficiency and pattern generation.
This paper detects market manipulation in Mt. Gox's Bitcoin exchange transactions.
problem Detecting market manipulation in cryptocurrency exchanges.
method Mining Mt. Gox's transaction history, categorizing accounts, constructing graphs, and using SVD for analysis.
result Identifies serious market manipulation in Mt. Gox exchange and recommends stronger market supervision.
Fine-tunes GNNs by preserving generative patterns to improve transferability.
problem Vanilla fine-tuning fails due to structural divergence between pre-training and downstream graphs.
method G-Tuning, which reconstructs the generative patterns of the downstream graph using graphon bases.
result G-Tuning achieves an average improvement of 0.5% and 2.6% on in-domain and out-of-domain transfer learning experiments.
Deep learning applied to biological data mining.
problem Mining complex biological data from diverse sources.
method Artificial neural networks, deep learning architectures.
result Deep learning techniques improve pattern recognition in biological data.
A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital f…
Randomization helps verify if data mining results are due to inherent patterns.
problem Verify if data mining results are due to inherent patterns or coincidental findings.
method Metropolis sampling based on local swaps to randomize data while preserving discovered patterns.
result Randomized data often reveals that clustering results imply frequent pattern discovery.
Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations wh…
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.
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.
This study identifies RwD crash patterns on rural two-lane highways under different lighting conditions.
problem Insufficient investigation of RwD crashes under varying lighting conditions.
method Data mining using association rules mining (ARM) on crash database.
result Interesting crash patterns and risk factors identified under different lighting conditions.
Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges…
This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact disc…
Graph neural networks detect anomalies in object-centric business processes.
problem Detecting anomalies in graph-like business processes.
method Graph convolutional autoencoder architecture for anomaly detection.
result Promising performance in detecting anomalies at the activity type and attributes level.
Multimodal clustering is an unsupervised technique for mining interesting patterns in -adic binary relations or -mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed $n…
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.
Finding "densely connected clusters" in a graph is in general an important and well studied problem in the literature \cite{Schaeffer}. It has various applications in pattern recognition, social networking and data mining \cite{Duda,Mishra}. Recently, Ames and Vavasis have suggested a novel method for finding cliques i…
EvoNet predicts the evolution of dynamic graphs using a graph neural network and recurrent architecture.
problem Predicting the evolution of dynamic graphs is challenging and underexplored.
method EvoNet uses a graph neural network and recurrent architecture to predict the evolution of dynamic graphs.
result EvoNet effectively predicts the evolution of dynamic graphs on both artificial and real-world datasets.
This paper proposes a framework to learn explainable rules from knowledge graphs for better recommendation.
problem Combining side information with explainability in recommendation systems.
method Joint learning framework integrating rule induction from knowledge graphs with a rule-guided neural recommendation model.
result Significant improvements in item recommendation performance over baselines.
Proposes a graph-based text representation for improved sentiment analysis.
problem Lack of effective methods to encode semantic relations in textual data for sentiment analysis.
method Sentence-level graph-based text representation with deep neural network.
result Significantly outperforms existing sentiment analysis approaches on benchmark datasets.
Chronnet models spatiotemporal data using chronological networks.
problem Handling large spatiotemporal datasets efficiently.
method Chronnet: Grid-based network model representing events chronologically.
result Chronnet captures frequent patterns, spatial changes, outliers, and clusters.
Karate Club simplifies graph mining for unsupervised learning.
problem Efficient unsupervised learning on graphs.
method API-oriented Python framework combining 30+ algorithms.
result Karate Club outperforms in learning performance and speed.
VisitHGNN predicts visit probabilities between neighborhoods and POIs using graph neural networks.
problem Estimating visit probabilities between neighborhoods and POIs for urban planning.
method Heterogeneous, relation-specific graph neural network (VisitHGNN) trained on mobility data.
result Strong predictive performance with high fidelity to observed travel behavior.
Mining frequent sequential patterns consists in extracting recurrent behaviors, modeled as patterns, in a big sequence dataset. Such patterns inform about which events are frequently observed in sequences, i.e. what does really happen. Sometimes, knowing that some specific event does not happen is more informative than…
Graph representation converts complex networks into vectors for easier analysis.
problem Efficient analysis of large network data.
method Introduces graph representation and network embedding models.
result Efficiently converts graph data into low-dimensional vectors.
The paper improves itemset quality assessment by incorporating background knowledge.
problem Assessing the quality of discovered itemsets is challenging due to many patterns being explainable by background knowledge.
method The authors introduce a maximum entropy approach to efficiently infuse additional background knowledge such as row margins, lazarus counts, and bounds of ones.
result More sophisticated models that incorporate background knowledge fit the data better and improve frequency prediction of itemsets.