Data Mining and Knowledge Discovery

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Guest Editors’ Introduction: Special issue of Selected Papers from ECML PKDD 2008
Data Mining and Knowledge Discovery - Tập 17 - Trang 1-2 - 2008
Walter Daelemans, Bart Goethals, Katharina Morik
Editorial
Data Mining and Knowledge Discovery - Tập 2 - Trang 5-7 - 1998
Usama Fayyad
An efficient procedure for mining egocentric temporal motifs
Data Mining and Knowledge Discovery - Tập 36 - Trang 355-378 - 2021
Giulia Cencetti, Antonio Longa, Bruno Lepri, Andrea Passerini
Temporal graphs are structures which model relational data between entities that change over time. Due to the complex structure of data, mining statistically significant temporal subgraphs, also known as temporal motifs, is a challenging task. In this work, we present an efficient technique for extracting temporal motifs in temporal networks. Our method is based on the novel notion of egocentric t...... hiện toàn bộ
Nhúng sự khác biệt cho hệ thống gợi ý Dịch bởi AI
Data Mining and Knowledge Discovery - Tập 37 - Trang 948-969 - 2022
Peng Yi, Xiongcai Cai, Ziteng Li
Bài báo này đề xuất một chiến lược huấn luyện điểm mới lạ và đơn giản, mang tên nhúng sự khác biệt (DifE), cho các hệ thống gợi ý nhằm nắm bắt thông tin cá nhân hóa được định hình từ sự khác biệt trong sở thích theo cặp, đồng thời sử dụng huấn luyện điểm hiệu quả và hiệu suất cao. Cụ thể, một hàm đã được thiết kế để nắm bắt và làm nổi bật sự khác biệt trong sở thích theo cặp. Sau đó, một phép chiế...... hiện toàn bộ
Exploiting the roles of aspects in personalized POI recommender systems
Data Mining and Knowledge Discovery - Tập 32 - Trang 320-343 - 2017
Ramesh Baral, Tao Li
The evolution of World Wide Web (WWW) and the smart-phone technologies have revolutionized our daily life. This has facilitated the emergence of many useful systems, such as Location-based Social Networks (LBSN) which have provisioned many factors that are crucial for selection of Point-of-Interests (POI). Some of the major factors are: (i) the location attributes, such as geo-coordinates, categor...... hiện toàn bộ
Using interesting sequences to interactively build Hidden Markov Models
Data Mining and Knowledge Discovery - Tập 21 - Trang 186-220 - 2010
Szymon Jaroszewicz
The paper presents a method of interactive construction of global Hidden Markov Models (HMMs) based on local sequence patterns discovered in data. The method is based on finding interesting sequences whose frequency in the database differs from that predicted by the model. The patterns are then presented to the user who updates the model using their intelligence and their understanding of the mode...... hiện toàn bộ
Simultaneous classification and community detection on heterogeneous network data
Data Mining and Knowledge Discovery - Tập 25 - Trang 420-449 - 2012
Prakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain
Previous studies on network mining have focused primarily on learning a single task (such as classification or community detection) on a given network. This paper considers the problem of multi-task learning on heterogeneous network data. Specifically, we present a novel framework that enables one to perform classification on one network and community detection in another related network. Multi-ta...... hiện toàn bộ
Finding density-based subspace clusters in graphs with feature vectors
Data Mining and Knowledge Discovery - Tập 25 - Trang 243-269 - 2012
Stephan Günnemann, Brigitte Boden, Thomas Seidl
Data sources representing attribute information in combination with network information are widely available in today’s applications. To realize the full potential for knowledge extraction, mining techniques like clustering should consider both information types simultaneously. Recent clustering approaches combine subspace clustering with dense subgraph mining to identify groups of objects that ar...... hiện toàn bộ
Reducing uncertainty of dynamic heterogeneous information networks: a fusing reconstructing approach
Data Mining and Knowledge Discovery - - 2017
Ning Yang, Lifang He, Zheng Li, Philip S. Yu
Exploring variable-length time series motifs in one hundred million length scale
Data Mining and Knowledge Discovery - Tập 32 - Trang 1200-1228 - 2018
Yifeng Gao, Jessica Lin
The exploration of repeated patterns with different lengths, also called variable-length motifs, has received a great amount of attention in recent years. However, existing algorithms to detect variable-length motifs in large-scale time series are very time-consuming. In this paper, we introduce a time- and space-efficient approximate variable-length motif discovery algorithm, Distance-Propagation...... hiện toàn bộ
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