2018 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第9页

Clustering Approach for Multidimensional Recommender Systems 多维推荐系统的聚类方法

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00161

Mohammed Wasid, R. Ali

引用次数: 5

Large Margin Graph Construction for Semi-Supervised Learning 半监督学习的大边界图构造

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00148

Lan-Zhe Guo, Shaozu Wang, Yu-Feng Li

引用次数: 1

Preserving Differential Privacy and Utility of Non-stationary Data Streams 保持非平稳数据流的差分隐私和效用

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00012

M. Khavkin, Mark Last

{"title":"Preserving Differential Privacy and Utility of Non-stationary Data Streams","authors":"M. Khavkin, Mark Last","doi":"10.1109/ICDMW.2018.00012","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00012","url":null,"abstract":"Data publishing poses many challenges regarding the efforts to preserve data privacy, on one hand, and maintain its high utility, on the other hand. The Privacy Preserving Data Publishing field (PPDP) has emerged as a possible solution to such trade-off, allowing data miners to analyze the published data, while providing a sufficient degree of privacy. Most existing anonymization platforms deal with static and stationary data, which can be scanned at least once before its publishing. More and more real-world applications generate streams of data which can be non-stationary, i.e., subject to a concept drift. In this paper, we introduce MiDiPSA (Microaggregation-based Differential Private Stream Anonymization) algorithm for non-stationary data streams, which aims at satisfying the constraints of k-anonymity, recursive (c, l)-diversity, and differential privacy while minimizing the information loss and the possible disclosure risk. The algorithm is implemented via four main steps: incremental clustering of the incoming tuples; incremental aggregation of the tuples in each cluster according to a pre-defined aggregation function; monitoring of the stream in order to detect possible concept drifts using a non-parametric Kolmogorov-Smirnov statistical test; and incremental publishing of anonymized tuples. Whenever a concept drift is detected, the clustering system is updated to reflect the current changes in the stream, without affecting the publishing process. In our empirical evaluation, we analyze the performance of various data stream classifiers on the anonymized data and compare it to their performance on the original data. We conduct experiments with seven benchmark data streams and show that our algorithm preserves privacy while providing higher utility, in comparison with other state-of-the-art anonymization algorithms.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131031213","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: 8

Path Reasoning over Knowledge Graph: A Multi-agent and Reinforcement Learning Based Method 知识图上的路径推理:基于多智能体和强化学习的方法

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00135

Zixuan Li, Xiaolong Jin, Saiping Guan, Yuanzhuo Wang, Xueqi Cheng

{"title":"Path Reasoning over Knowledge Graph: A Multi-agent and Reinforcement Learning Based Method","authors":"Zixuan Li, Xiaolong Jin, Saiping Guan, Yuanzhuo Wang, Xueqi Cheng","doi":"10.1109/ICDMW.2018.00135","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00135","url":null,"abstract":"Relation reasoning over knowledge graphs is an important research problem in the fields of knowledge engineering and artificial intelligence, because of its extensive applications (e.g., knowledge graph completion and question answering). Recently, reinforcement learning has been successfully applied to multi-hop relation reasoning (i.e., path reasoning). And, a kind of practical path reasoning, in the form of query answering (e.g., (entity, relation, ?)), has been proposed and attracted much attention. However, existing methods for such type of path reasoning focus on relation selection and underestimate the importance of entity selection during the reasoning process. To solve this problem, we propose a Multi-Agent and Reinforcement Learning based method for Path Reasoning, thus called MARLPaR, where two agents are employed to carry out relation selection and entity selection, respectively, in an iterative manner, so as to implement complex path reasoning. Experimental comparison with the state-of-the-art baselines on two benchmark datasets validates the effectiveness and merits of the proposed method.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128716630","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: 16

Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity 用软路由和学习连接有效地生长深森林

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00065

Jianghao Shen, Sicheng Wang, Zhangyang Wang

{"title":"Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity","authors":"Jianghao Shen, Sicheng Wang, Zhangyang Wang","doi":"10.1109/ICDMW.2018.00065","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00065","url":null,"abstract":"Despite the latest prevailing success of deep neural networks (DNNs), several concerns have been raised against their usage, including the lack of intepretability the gap between DNNs and other well-established machine learning models, and the growingly expensive computational costs. A number of recent works [1],[2],[3] explored the alternative to sequentially stacking decision tree/random forest building blocks in a purely feed-forward way, with no need of back propagation. Since decision trees enjoy inherent reasoning transparency, such deep forest models can also facilitate the understanding of the internal decision making process. This paper further extends the deep forest idea in several important aspects. Firstly, we employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., \"soft routing\", rather than hard binary decisions. Besides enhancing the flexibility, it also enables non-greedy optimization for each tree. Second, we propose an innovative topology learning strategy: every node in the ree now maintains a new learnable hyperparameter indicating the probability that it will be a leaf node. In that way, the tree will jointly optimize both its parameters and the tree topology during training. Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3], with dramatically reduced model complexity. For example, our model with only 1 layer of 15 trees can perform comparably with the model in [3] with 2 layers of 2000 trees each.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131368061","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

Effective Steering of Customer Journey via Order-Aware Recommendation 通过订单感知推荐有效地引导客户旅程

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00123

J. Goossens, T. Demewez, Marwan Hassani

{"title":"Effective Steering of Customer Journey via Order-Aware Recommendation","authors":"J. Goossens, T. Demewez, Marwan Hassani","doi":"10.1109/ICDMW.2018.00123","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00123","url":null,"abstract":"The analysis of customer journeys is a subject undergoing an intense study recently. The increase in understanding of customer behaviour serves as an important source of success to many organizations. Current research is however mostly focussed on visualizing these customer journeys to allow them to be more interpretable by humans. A deeper use of customer journey information in prediction and recommendation processes has not been achieved. This paper aims to take a step forward into that direction by introducing the Order-Aware Recommendation Approach (OARA). The main scientific contributions showcased by this approach are (i) increasing performance on prediction and recommendation tasks by taking into account the explicit order of actions in the customer journey, (ii) showing how a visualization of a customer journey can play an important role during predictions and recommendations, and (iii) introducing a way of maximizing recommendations for any tailor-made Key Performance Indicator (KPI) instead of the accuracy-based metrics traditionally used for this task. An extensive experimental evaluation study highlights the potential of OARA against state-of-the-art approaches using a real dataset representing a customer journey of upgrading with multiple products.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125614042","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: 8

Ensemble Cross-Conformal Prediction 集合交叉保角预测

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00128

Dorian Beganovic, E. Smirnov

引用次数: 6

Congressional Vote Analysis Using Signed Networks 使用签名网络进行国会投票分析

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00218

Tyler Derr, Jiliang Tang

{"title":"Congressional Vote Analysis Using Signed Networks","authors":"Tyler Derr, Jiliang Tang","doi":"10.1109/ICDMW.2018.00218","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00218","url":null,"abstract":"In today's era of big data, much can be represented as a network. However, most of the work in traditional network analysis is unable to handle many existing network types, which is due to certain networks having added complexities. For example, signed networks, which have both positive and negative links, have been shown to require dedicated efforts due to the methods designed for typical unsigned networks (those having only positive links) being no longer applicable. One specific type of signed network is that of voting records, such as the Senate and House of Representatives from the U.S. Congress, which form signed bipartite networks between the congresspeople and the bills voted upon. With the current tensions between the two prominent political parties in the U.S., it seems time to ask the question if signed network analysis methods are able to aid in our understanding of the underlying dynamics of the voting habits in the U.S. Congress, since they drive some of the most influential decision making processes in the country. To this end, in this paper, we conduct a thorough analysis on the behaviors of both current and past U.S. Congress voting datasets uncovering numerous patterns, extending and then investigating the applicability of balance theory in the signed bipartite setting, and then finally leverage our findings to accurately predict the sign of missing links.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115650027","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: 9

Within-Network Classification in Temporal Graphs 时态图的网络内分类

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00041

C. Ryther, J. Simonsen

引用次数: 0

Analyzing Centralities of Embedded Nodes 嵌入式节点的中心性分析

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00151

Kento Nozawa, Masanari Kimura, Atsunori Kanemura

引用次数: 0