数据收集、存储和分发方面的进步已经产生了对帮助数据分析的计算工具和技术的需求。数据库中的数据挖掘和知识发现 (KDD) 是一个快速发展的研究和应用领域，它建立在许多领域的技术和理论之上，包括统计学、数据库、模式识别和学习、数据可视化、不确定性建模、数据仓库和 OLAP，优化和高性能计算。

With advancements in data collection, storage, and distribution technologies, there is a growing demand for tools and techniques that assist in data analysis. Data Mining and Knowledge Discovery (KDD) is a rapidly evolving field of research and application that builds upon technologies and theories from various disciplines, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modeling, data warehousing and OLAP, optimization, and high-performance computing.

随着数据收集、存储和分发技术的进步，市场对数据分析工具和技术的需求日益增长。数据挖掘与知识发现（Data Mining and Knowledge Discovery，KDD）是一个快速发展的研究与应用领域，融合了统计学、数据库、模式识别与学习、数据可视化、不确定性建模、数据仓库与OLAP、优化以及高性能计算等多个学科的技术和理论。

Data Mining and Knowledge Discovery

Antonis Matakos

Evimaria Terzi

Panayiotis Tsaparas

Measuring and moderating opinion polarization in social networks

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.

Jiawei Han

Jian Pei

Yiwen Yin

Runying Mao

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

不产生候选模式的频繁模式挖掘：一种频繁模式树方法

Yan Liu

Xue Feng

Jun Lou

Lianyu Hu

Zengyou He

Central node identification via weighted kernel density estimation

加权核密度估计的中心节点识别

Abstract Graph pooling methods provide mechanisms for structure reduction that are intended to ease the diffusion of context between nodes further in the graph, and that typically leverage community discovery mechanisms or node and edge pruning heuristics. In this paper, we introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity patterns. Our pooling method, named KPlexPool , builds on the concepts of graph covers and k -plexes, i.e. pseudo-cliques where each node can miss up to k links. The experimental evaluation on benchmarks on molecular and social graph classification shows that KPlexPool achieves state of the art performances against both parametric and non-parametric pooling methods in the literature, despite generating pooled graphs based solely on topological information.

Davide Bacciu

Alessio Conte

Roberto Grossi

Francesco Landolfi

Andrea Marino

K-plex cover pooling for graph neural networks

图神经网络的K-plex覆盖池化

Kun Kuang

Yunzhe Li

Bo Li

Peng Cui

Hongxia Yang

Jianrong Tao

Fei Wu

Continuous treatment effect estimation via generative adversarial de-confounding

Anton D. Lautrup

Tobias Hyrup

Arthur Zimek

Peter Schneider-Kamp

Syntheval: a framework for detailed utility and privacy evaluation of tabular synthetic data

Syntheval：一种用于表格合成数据详细效用和隐私评估的框架

Nan Xia

Yin Wang

Run-Fa Zhang

Xiangfeng Luo

ARL: analogical reinforcement learning for knowledge graph reasoning

ARL：知识图谱推理的类比强化学习

Lamprini Pappa

Petros Karvelis

Chrysostomos Stylios

Exploring the diverse world of SAX-based methodologies

探索基于SAX方法论的多样世界

Gustavo Lúcius Fernandes

Flavio Figueiredo

Raphael Siston Hatushika

Maria Luiza Leão

Breno Augusto Mariano

Bruno Augusto Alemão Monteiro

Fernando Tonucci de Cerqueira Oliveira

Tales Panoutsos

João Pedro Pires

Thiago Martin Poppe

Frederico Zavam

A systematic review of deep learning for structural geological interpretation

深度学习在构造地质解释中的系统综述

Abstract Anomaly detection has become pervasive in modern technology, covering applications from cybersecurity, to medicine or system failure detection. Before outputting a binary outcome (i.e., anomalous or non-anomalous), most algorithms evaluate instances with outlierness scores. But what does a score of 0.8 mean? Or what is the practical difference compared to a score of 1.2? Score ranges are assumed non-linear and relative, their meaning established by weighting the whole dataset (or a dataset model). While this is perfectly true, algorithms also impose dynamics that decisively affect the meaning of outlierness scores. In this work, we aim to gain a better understanding of the effect that both algorithms and specific data particularities have on the meaning of scores. To this end, we compare established outlier detection algorithms and analyze them beyond common metrics related to accuracy. We disclose trends in their dynamics and study the evolution of their scores when facing changes that should render them invariant. For this purpose we abstract characteristic S -curves and propose indices related to discriminant power, bias, variance, coherence and robustness. We discovered that each studied algorithm shows biases and idiosyncrasies, which habitually persist regardless of the dataset used. We provide methods and descriptions that facilitate and extend a deeper understanding of how the discussed algorithms operate in practice. This information is key to decide which one to use, thus enabling a more effective and conscious incorporation of unsupervised learning in real environments.

Abbreviation	Data Min Knowl Disc
Journal Impact	2.86
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q2)

ISSN	1384-5810, 1573-756X
h-index	117

Publication Information	Publisher: Springer Netherlands，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1997，Proportion of original research papers: 98.91%，Self Citation Rate:0.00%， Gold OA Rate: 44.96%
Average review cycle	网友分享经验：平均6.0个月
Average recruitment ratio	网友分享经验：较易

Data Mining and Knowledge Discovery

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