A Novel Method on Information Recommendation via Hybrid Similarity

Zhao, Qin; Wang, Cheng; Wang, Pengwei; Zhou, MengChu; Jiang, Changjun

doi:10.1109/tsmc.2016.2633573

Cited by 42 publications

(23 citation statements)

References 29 publications

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“…• RL+C method: A hybrid method that ranks the topk traders based on the return loss and the consistency of the traders. To handle the uncertainty of traders' behavior and performances [27], it is helpful to estimate traders from multiple aspects in a hybrid manner [28]. In the RL+C method, the ranking score of a trader i during m months is calculated as follows:…”

Section: Methodsmentioning

confidence: 99%

Portfolio-Based Ranking of Traders for Social Trading

Liu

2020

IEEE Access

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Social trading is a social network service (SNS) trading platform where users can share their investment experience and knowledge. One of the notable features of social trading is that this platform allows users to imitate the transactions conducted by other traders simply by following them. However, finding and following expert traders is the key challenge of social trading. To this end, systems and methods have been developed to rank traders. One of the drawbacks of the conventional methods is that there is no financial theory to support them. In this paper, a novel ranking method to discover expert traders based on the portfolio theory, is proposed. The experimental results obtained with a real dataset demonstrate that this method is superior to conventional methods. In addition, the discovered expert traders can be enlisted to develop better portfolio constructions for any given risk level.

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Section: Methodsmentioning

confidence: 99%

Portfolio-Based Ranking of Traders for Social Trading

Liu

2020

IEEE Access

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“…Many indexes, like jaccard , only concentrate on the proportional relation between the same neighbors and the whole neighbors ignoring the differences between each nodes [31], [32]. While the AA index [27] takes the differences between nodes into account, but the scarcity of proportional relationship leads to a great deal of uncertainty in the result, for example, in some cases the result of low-degree node is much more higher than the high-degree node which has more same neighbor [34].…”

Section: ) Similarity Indexesmentioning

confidence: 99%

A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

Xia

Zhang

et al. 2020

IEEE Access

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The explosive growth of Internet of Things (i.e., IoT) terminal equipment makes its topology more complex, which leads to the increasing cost of network research. Recently, the implicit community structure is widely used to improve the efficiency of research. However, most of the non-overlapping community detection algorithms have some weakness, such as the large number of community detected and the obvious scale gap between communities. To address these abovementioned problems, we design a novel non-overlapping community detection algorithm, named as Pairing, Splitting and Aggregating algorithm (i.e., PSA). Firstly, in order to improve the accuracy of community division, a new node similarity index is designed to transform the network into a large number of similar node pairs. Secondly, based on the connected branches composed of similar node pairs, the network is further divided into several similar node sets. Thirdly, to balance the scale gap of different communities, the Grasshopper Optimization Algorithm, (i.e., GOA) is introduced to combine the local attribute (i.e., conductance) and global attribute (i.e., modularity) together to aggregate similar node sets into potential (or final) communities. Finally, the experimental results show that PSA not only controls the difference among communities well, but also outperforms the other four popular algorithms in terms of two metrics. Moreover, we propose a community-based resource discovery method (or scheme), named as Community-assisted Short-distancequery Resource Discovery algorithm (i.e., CSRD) to further verify the efficiency of PSA. The results show that the resource discovery efficiency of CSRD using PSA is better compared with other algorithms.

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“…D ATA clustering is an unsupervised learning technique that aims to partition a set of data objects (i.e., data points) into a certain number of homogeneous groups [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]. It is a fundamental yet very challenging topic in the field of data mining and machine learning, and has been successfully applied in a wide variety of areas, such as image processing [11], [12], community discovery [13], [14], recommender systems [15], [16], [17] and text mining [18]. In the past few decades, a large number of clustering algorithms have been developed by exploiting various techniques [19].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities

Huang

Wang

Peng

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

171

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Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues in the current ensemble clustering research. First, most of the existing algorithms tend to investigate the ensemble information at the object-level, yet often lack the ability to explore the rich information at higher levels of granularity. Second, they mostly focus on the direct connections (e.g., direct intersection or pairwise co-occurrence) in the multiple base clusterings, but generally neglect the multi-scale indirect relationship hidden in them. To address these two issues, this paper presents a novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks. We first construct a cluster similarity graph with the base clusters treated as graph nodes and the cluster-wise Jaccard coefficient exploited to compute the initial edge weights. Upon the constructed graph, a transition probability matrix is defined, based on which the random walk process is conducted to propagate the graph structural information. Specifically, by investigating the propagating trajectories starting from different nodes, a new cluster-wise similarity matrix can be derived by considering the trajectory relationship. Then, the newly obtained cluster-wise similarity matrix is mapped from the cluster-level to the object-level to achieve an enhanced co-association (ECA) matrix, which is able to simultaneously capture the object-wise co-occurrence relationship as well as the multi-scale cluster-wise relationship in ensembles. Finally, two novel consensus functions are proposed to obtain the consensus clustering result. Extensive experiments on a variety of real-world datasets have demonstrated the effectiveness and efficiency of our approach.

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A Novel Method on Information Recommendation via Hybrid Similarity

Cited by 42 publications

References 29 publications

Portfolio-Based Ranking of Traders for Social Trading

Portfolio-Based Ranking of Traders for Social Trading

A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities

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