2022 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献

Domain Adaptation Through Cluster Integration and Correlation 基于聚类集成和关联的领域自适应

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00025

Vishnu Manasa Devagiri, V. Boeva, Shahrooz Abghari

{"title":"Domain Adaptation Through Cluster Integration and Correlation","authors":"Vishnu Manasa Devagiri, V. Boeva, Shahrooz Abghari","doi":"10.1109/ICDMW58026.2022.00025","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00025","url":null,"abstract":"Domain shift is a common problem in many real-world applications using machine learning models. Most of the existing solutions are based on supervised and deep-learning models. This paper proposes a novel clustering algorithm capable of producing an adapted and/or integrated clustering model for the considered domains. Source and target domains are represented by clustering models such that each cluster of a domain models a specific scenario of the studied phenomenon by defining a range of allowable values for each attribute in a given data vector. The proposed domain integration algorithm works in two steps: (i) cross-labeling and (ii) integration. Initially, each clustering model is crossly applied to label the cluster representatives of the other model. These labels are used to determine the correlations between the two models to identify the common clusters for both domains, which must be integrated within the second step. Different features of the proposed algorithm are studied and evaluated on a publicly available human activity recognition (HAR) data set and real-world data from a smart logistics use case provided by an industrial partner. The experiment's goal on the HAR data set is to showcase the algorithm's potential in automatic data labeling. While the conducted experiments on the smart logistics use case evaluate and compare the performance of the integrated and two adapted models in different domains.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127338379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Graph Neural Network with Learnable Permutation Pooling 基于可学习置换池的图神经网络改进

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00094

Yu Jin, J. JáJá

引用次数: 0

Simplifying Process Navigations - Divide and Rule way 简化过程导航-分而治之的方式

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00020

Sai Charan Emmadi, Satya Samudrala, Parag Agrawal, M. Natu

引用次数: 0

Cascaded Multi-Class Network Intrusion Detection With Decision Tree and Self-attentive Model 基于决策树和自关注模型的级联多类网络入侵检测

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00081

Yuchen Lan, Tram Truong-Huu, Ji-Yan Wu, S. Teo

{"title":"Cascaded Multi-Class Network Intrusion Detection With Decision Tree and Self-attentive Model","authors":"Yuchen Lan, Tram Truong-Huu, Ji-Yan Wu, S. Teo","doi":"10.1109/ICDMW58026.2022.00081","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00081","url":null,"abstract":"Network intrusion has become a leading threat to breaching the security of Internet applications. With the reemergence of artificial intelligence, deep neural networks (DNN) have been widely used for network intrusion detection. However, one main problem with the DNN models is the dependency on sufficient high-quality labeled data to train the model to achieve decent accuracy. DNN models may incur many false predictions on the imbalanced intrusion datasets, especially on the minority classes. While we continue advocating for using machine learning and deep learning for network intrusion detection, we aim at addressing the drawback of existing DNN models by effectively integrating decision tree and feature tokenizer (FT)-transformer. First, the decision tree algorithm is used for the binary classification of regular (normal) traffic and malicious traffic. Second, FT-transformer performs the multi-category classification on that malicious traffic to identify the type of attacking traffic. We conduct the performance evaluation using three publicly available datasets: CIC-IDS 2017, UNSW-NB15, and Kitsune datasets. Experimental results show that among three datasets, the proposed technique achieves the best performance on the CIC-IDS 2017 dataset with the macro precision, recall, and F1-score of 84.6%, 83.6%, and 93.2%, respectively.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129042131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Solving Non-linear Optimization Problem in Engineering by Model-Informed Generative Adversarial Network (MI-GAN) 基于模型信息的生成对抗网络(MI-GAN)求解工程非线性优化问题

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00035

Yuxuan Li, Chaoyue Zhao, Chenang Liu

{"title":"Solving Non-linear Optimization Problem in Engineering by Model-Informed Generative Adversarial Network (MI-GAN)","authors":"Yuxuan Li, Chaoyue Zhao, Chenang Liu","doi":"10.1109/ICDMW58026.2022.00035","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00035","url":null,"abstract":"Optimization models have been widely used in many engineering systems to solve the problems related to system operation and management. For instance, in power systems, the optimal power flow (OPF) problem, which is a critical component of power system operations, can be formulated using optimization models. Specifically, the alternating current OPF (AC-OPF) problems are challenging since some of the constraints are non-linear and non-convex. Moreover, due to the high variability that the power system may have, the coefficients of the optimization model may change, increasing the difficulty of solving the OPF problem. Although the conventional optimization tools and deep learning approaches have been investigated, the feasibility and optimality of the solutions may still be unsatisfactory. Hence, in this paper, based on the recently developed model-informed generative adversarial network (MI-GAN) framework, a tailored version for solving the non-linear AC-OPF problem under uncertainties is proposed. The contributions of this work can be summarized into two main aspects: (1) To ensure the feasibility and improve the optimality of the generated solutions, two important layers, namely, the feasibility filter layer and optimality-filter layer, are considered and designed; and (2) An efficient model-informed selector is designed and integrated to the GAN architecture, by incorporating these two new layers to inform the generator. Experiments on the IEEE test systems demonstrate the efficacy and potential of the proposed method for solving non-linear AC-OPF problems.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126793797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Next POI Recommender System: Multi-view Representation Learning for Outstanding Performance in Various Context 下一个POI推荐系统:多视图表示学习在各种环境下的卓越表现

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00150

Yeonghwan Jeon, Junhyung Kim

{"title":"Next POI Recommender System: Multi-view Representation Learning for Outstanding Performance in Various Context","authors":"Yeonghwan Jeon, Junhyung Kim","doi":"10.1109/ICDMW58026.2022.00150","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00150","url":null,"abstract":"Location-based Social Networks (LBSNs) are software service that enable a user to find knowledge and to socialize with other users by offering other user's contents (e.g. reviews, photos, etc.) to a user. This LBSNs have many sub-fields, but Point-of-Interest (POI) recommendation is the most important. Because it is related to the growth of Small and Medium Enterprise (SME) by increasing visitation rate. Generally, it should be possible to respond to various contexts of users in POI recommendation. These contexts are very various and complex, but we define mainly three contexts based on user behavior in local domain. However, each context is defined by different user behavior, so each model and performance are different on various evaluation criteria. In other words, no model is outstanding in all contexts. Therefore, this paper introduces how to define each context, how to make POI embedding for recommendation in empirical multi-view representation learning technique, and how to make optimized POI embedding which is outstanding performance in all contexts of POI recommendation, for various downstream tasks.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116321863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Edit distance with Quasi Real Penalties: a hybrid distance for network-constrained trajectories 带准实惩罚的编辑距离:网络约束轨迹的混合距离

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00136

Noudéhouénou Lionel Jaderne Houssou, Jean-Loup Guillaume, A. Prigent

{"title":"Edit distance with Quasi Real Penalties: a hybrid distance for network-constrained trajectories","authors":"Noudéhouénou Lionel Jaderne Houssou, Jean-Loup Guillaume, A. Prigent","doi":"10.1109/ICDMW58026.2022.00136","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00136","url":null,"abstract":"In this paper, we propose a new distance for network-constrained trajectories named Edit distance with Quasi Real Penalties (EQRP). Depending on the case, it can compare trajectories as non-ordered sets and as sequences while other distances only compare trajectories as non-ordered sets or as sequences. Moreover, it is parameter-free, manages local time shifting, and respects triangle inequality; three properties expected from a trajectory distance that are not satisfied simultaneously by any other distance to the best of our knowledge. To demonstrate the pertinence of our idea, we benchmark our distance against some state-of-the-art distances for network-constrained trajectories. Specifically, for each distance, we determine its capability to identify precisely similar trajectories. We also determine their respective performance for trajectory clustering. Our results show the predominance of EQRP over the existing edit distances and in some cases a more precise ability to evaluate the dissimilarity between network-constrained trajectories compared to other measures.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126458438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nearest neighbors with incremental learning for real-time forecasting of electricity demand 最近的邻居，增量学习，实时预测电力需求

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00112

Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, A. T. Lora

{"title":"Nearest neighbors with incremental learning for real-time forecasting of electricity demand","authors":"Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, A. T. Lora","doi":"10.1109/ICDMW58026.2022.00112","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00112","url":null,"abstract":"Electricity demand forecasting is very useful for the different actors involved in the energy sector to plan the supply chain (generation, storage and distribution of energy). Nowadays energy demand data are streaming data coming from smart meters and has to be processed in real-time for more efficient demand management. In addition, this kind of data can present changes over time such as new patterns, new trends, etc. Therefore, real-time forecasting algorithms have to adapt and adjust to online arriving data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand forecasting in real-time. The proposed algorithm generates a prediction model based on the K-nearest neighbors algorithm, which is incrementally updated as online data arrives. Both time-frequency and error threshold based model updates have been evaluated. Results using Spanish electricity demand data with a ten-minute sampling frequency rate are reported, reaching 2% error with the best prediction model obtained when the update is daily.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128202799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Abnormal Entity-Aware Knowledge Graph Completion 异常实体感知知识图谱补全

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00118

Keyi Sun, Shuo Yu, Ciyuan Peng, Xiang Li, Mehdi Naseriparsa, Feng Xia

{"title":"Abnormal Entity-Aware Knowledge Graph Completion","authors":"Keyi Sun, Shuo Yu, Ciyuan Peng, Xiang Li, Mehdi Naseriparsa, Feng Xia","doi":"10.1109/ICDMW58026.2022.00118","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00118","url":null,"abstract":"In real-world scenarios, knowledge graphs remain incomplete and contain abnormal information, such as redundan-cies, contradictions, inconsistencies, misspellings, and abnormal values. These shortcomings in the knowledge graphs potentially affect service quality in many applications. Although many approaches are proposed to perform knowledge graph completion, they are incapable of handling the abnormal information of knowledge graphs. Therefore, to address the abnormal information issue for the knowledge graph completion task, we design a novel knowledge graph completion framework called ABET, which specially focuses on abnormal entities. ABET consists of two components: a) abnormal entity prediction and b) knowledge graph completion. Firstly, the prediction component automati-cally predicts the abnormal entities in knowledge graphs. Then, the completion component effectively captures the heterogeneous structural information and the high-order features of neighbours based on different relations. Experiments demonstrate that ABET is an effective knowledge graph completion framework, which has made significant improvements over baselines. We further verify that ABET is robust for knowledge graph completion task with abnormal entities.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131047946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Influence Maximization on Hypergraphs via Similarity-based Diffusion 基于相似度扩散的超图影响最大化

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00158

M. E. Aktas, Sidra Jawaid, Ihsan Gokalp, Esra Akbas

{"title":"Influence Maximization on Hypergraphs via Similarity-based Diffusion","authors":"M. E. Aktas, Sidra Jawaid, Ihsan Gokalp, Esra Akbas","doi":"10.1109/ICDMW58026.2022.00158","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00158","url":null,"abstract":"Influence maximization is an important problem in network science that aims to detect critical structures, such as nodes and interactions, with a higher influence on diffusion. It has applications in information spreading, rumor controlling, marketing, disease spreading, advertising, and more. Although the influence maximization problem in graphs has been studied ex-tensively, there are a few studies that explore critical structures in hypergraphs and these studies mostly focus on detecting influential nodes rather than higher-order interactions, i.e., hyperedges. In this paper, we study the influential hyperedge detection problem. We first design diffusion models on hypergraphs based on the similarity between hyperedges. Our claim here is that similarity between hyperedges is positively correlated with the diffusion process. To study this claim, we first calculate similarity scores between hyperedges and construct similarity-based hypergraph Laplacians. Next, we extend standard graph centrality measures for hyperedges using these Laplacians. We compare the similarity- based hypergraph Laplacians with the state-of-the-art influential hyperedge detection method using two evaluation metrics: the size of the giant component and the Susceptible-Infected-Recovered (SIR) simulation model. Our experimental results suggest that overall, similarity-based Laplacians are more effective than the state-of-the-art method in finding influential higher-order hyperedges.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133357932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1