Data Mining and Knowledge Discovery

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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
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
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
Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series
Data Mining and Knowledge Discovery - - 2015
Bing Hu, Thanawin Rakthanmanon, Yuan Hao, S. Evans, Stefano Lonardi, Eamonn Keogh
Hypergraph Models and Algorithms for Data-Pattern-Based Clustering
Data Mining and Knowledge Discovery - Tập 9 - Trang 29-57 - 2004
Muhammet Mustafa Ozdal, Cevdet Aykanat
In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying t...... hiện toàn bộ
An adaptive algorithm for anomaly and novelty detection in evolving data streams
Data Mining and Knowledge Discovery - - 2018
Mohamed-Rafik Bouguelia, Sławomir Nowaczyk, Amir H. Payberah
Multiple Bayesian discriminant functions for high-dimensional massive data classification
Data Mining and Knowledge Discovery - Tập 31 - Trang 465-501 - 2016
Jianfei Zhang, Shengrui Wang, Lifei Chen, Patrick Gallinari
The presence of complex distributions of samples concealed in high-dimensional, massive sample-size data challenges all of the current classification methods for data mining. Samples within a class usually do not uniformly fill a certain (sub)space but are individually concentrated in certain regions of diverse feature subspaces, revealing the class dispersion. Current classifiers applied to such ...... hiện toàn bộ
A Mathematical Morphology Based Scale Space Method for the Mining of Linear Features in Geographic Data
Data Mining and Knowledge Discovery - Tập 12 - Trang 97-118 - 2006
Min Wang, Yee Leung, Chenhu Zhou, Tao Pei, Jiancheng Luo
This paper presents a spatial data mining method MCAMMO and its extension L_MCAMMO designed for discovering linear and near linear features in spatial databases. L_MCAMMO can be divided into two basic steps: first, the most suitable re-segmenting scale is found by MCAMMO, which is a scale space method with mathematical morphology operators; second, the segmented result at this scale is re-segmente...... hiện toàn bộ
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