Similar Neighbors Research Articles

A matrix factorization recommendation model for tourism points of interest based on interest shift and differential privacy

Adding noise to user history data helps to protect user privacy in recommendation systems but affects the recommendation performance. To solve this problem, a matrix factorization tourism point of interest recommendation model based on interest offset and differential privacy is proposed in this paper. The recommendation performance of the model is improved by analyzing user interest preferences. Specifically, user interest offsets are extracted from user tags and user ratings under time-series factors to calculate user interest drift. Then, similar neighbors are found to train user feature preferences which are integrated into the matrix model in the form of regular terms. Meanwhile, based on the differential privacy theory, a privacy neighbor selection algorithm combining the K-Medoides clustering algorithm and index mechanism is designed to effectively protect the identity of neighbors and prevent KNN attacks. Besides, the Laplace mechanism is used to implement differential privacy protection for the model’s gradient descent process. Finally, the feasibility of the proposed recommendation model is verified through experiments, and the experimental results indicate that this model has advantages in recommendation accuracy and privacy protection.

3D Echocardiogram Reconstruction Employing a Flip Directional Texture Pyramid

Three dimensional (3D) echocardiogram enables cardiologists to visualize suspicious cardiac structures in detail. In recent years, this three-dimensional echocardiogram carries important clinical value in virtual surgical simulation. However, this 3D echocardiogram involves a trade-off difficulty between accuracy and efficient computation in clinical diagnosis. This paper presents a novel Flip Directional 3D Volume Reconstruction (FD-3DVR) method for the reconstruction of echocardiogram images. The proposed method consists of two main steps: multiplanar volumetric imaging and 3D volume reconstruction. In the creation of multiplanar volumetric imaging, two-dimensional (2D) image pixels are mapped into voxels of the volumetric grid. As the obtained slices are discontinuous, there are some missing voxels in the volume data. To restore the structural and textural information of 3D ultrasound volume, the proposed method creates a volume pyramid in parallel with the flip directional texture pyramid. Initially, the nearest neighbors of missing voxels in the multiplanar volumetric imaging are identified by 3D ANN (Approximate Nearest Neighbor) patch matching method. Furthermore, a flip directional texture pyramid is proposed and aggregated with distance in patch matching to find out the most similar neighbors. In the reconstruction step, structural and textural information obtained from different flip angle directions can reconstruct 3D volume well with the desired accuracy. Compared with existing 3D reconstruction methods, the proposed Flip Directional 3D Volume Reconstruction (FD-3DVR) method provides superior performance for the mean peak signal-to-noise ratio (40.538 for the proposed method I and 39.626 for the proposed method II). Experimental results performed on the cardiac datasets demonstrate the efficiency of the proposed method for the reconstruction of echocardiogram images.

Adaptive Hypergraph Regularized Multilayer Sparse Tensor Factorization for Hyperspectral Unmixing

Hyperspectral unmixing with tensor models has received great attention in recent years. A tensor-based decomposition method can effectively represent the structural feature of hyperspectral images; however, the obtained results may be physically uninterpretable. To overcome this limitation, a novel adaptive hypergraph regularized multilayer sparse tensor factorization (AHGMLSTF) algorithm is proposed. First, a modified hypergraph is incorporated into tensor factorization, and the modified hypergraph uses spectral angle distance (SAD) instead of Euclidean distance to construct hyperedges to better represent the joint spatial and spectral information. Then, the hypergraph is constructed adaptively by hyperedges of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> neighborhoods. Second, the concept of multilayer decomposition is introduced to explore the hierarchical features of hyperspectral images, and a sparse constraint is imposed on each layer to make the unmixing results more consistent with the physical mechanism of mixed spectral pixels. With these constraints, the proposed method established a spectral–spatial joint tensor decomposition model that represents not only the local neighborhood similarity but also the heterogeneity of adjacent edges. Experiments on simulated data and real hyperspectral data demonstrate the effectiveness of the proposed method.

A Method for Remote Sensing Image Classification by Combining Pixel Neighbourhood Similarity and Optimal Feature Combination

In the study of remote sensing image classification, feature extraction and selection is an effective method to distinguish different classification targets. Constructing a high-quality spectral-spatial feature and feature combination has been a worthwhile topic for improving classification accuracy. In this context, this study constructed a spectral-spatial feature, namely the Pixel Neighbourhood Similarity (PNS) index. Meanwhile, the PNS index and 19 spectral, textural and terrain features were involved in the Correlation-based Feature Selection (CFS) algorithm for feature selection to generate a feature combination (PNS-CFS). To explore how PNS and PNS-CFS improve the classification accuracy of land types. The results show that: (1) The PNS index exhibited clear boundaries between different land types. The performance quality of PNS was relatively highest compared to other spectral-spatial features, namely the Vector Similarity (VS) index, the Change Vector Intensity (CVI) index and the Correlation (COR) index. (2) The Overall Accuracy (OA) of the PNS-CFS was 94.66% and 93.59% in study areas 1 and 2, respectively. These were 7.48% and 6.02% higher than the original image data (ORI) and 7.27% and 2.39% higher than the single-dimensional feature combination (SIN-CFS). Compared to the feature combinations of VS, CVI, and COR indices (VS-CFS, CVI-COM, COR-COM), PNS-CFS had the relatively highest performance and classification accuracy. The study demonstrated that the PNS index and PNS-CFS have a high potential for image classification.

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Medical image is an essential tool used in quantitative and qualitative evaluation of different diseases. Medical imaging methods such as fluorescein angiography (FA), optical coherence tomography angiography (OCTA), computed tomography (CT), optical coherence tomography (OCT), and X-ray are used for diagnosis. These imaging modalities suffer from low contrast, which leads to deterioration in the image quality. Consequently, this causes limitation in the usage of medical images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision-making. To overcome this limitation, we propose a novel local transfer function for medical image enhancement algorithm using the pixel neighborhood constraint. The proposed algorithm uses block-wise intensity distribution to generate the regional similarity index. The regional similarity index transformed each centered pixel in the block, to generate a new similarity image. An intuitive optimization algorithm is utilized to optimize the proposed algorithm parameters. Experimentation results show that the proposed LTF-NSI performs better than the state-of-the-art methods and improves the interpretability and perception of the medical images, which can provide clinicians and computer vision program with good quantitative and qualitative information.

Improving automatic GO annotation with semantic similarity

BackgroundAutomatic functional annotation of proteins is an open research problem in bioinformatics. The growing number of protein entries in public databases, for example in UniProtKB, poses challenges in manual functional annotation. Manual annotation requires expert human curators to search and read related research articles, interpret the results, and assign the annotations to the proteins. Thus, it is a time-consuming and expensive process. Therefore, designing computational tools to perform automatic annotation leveraging the high quality manual annotations that already exist in UniProtKB/SwissProt is an important research problemResultsIn this paper, we extend and adapt the GrAPFI (graph-based automatic protein function inference) (Sarker et al. in BMC Bioinform 21, 2020; Sarker et al., in: Proceedings of 7th international conference on complex networks and their applications, Cambridge, 2018) method for automatic annotation of proteins with gene ontology (GO) terms renaming it as GrAPFI-GO. The original GrAPFI method uses label propagation in a similarity graph where proteins are linked through the domains, families, and superfamilies that they share. Here, we also explore various types of similarity measures based on common neighbors in the graph. Moreover, GO terms are arranged in a hierarchical manner according to semantic parent–child relations. Therefore, we propose an efficient pruning and post-processing technique that integrates both semantic similarity and hierarchical relations between the GO terms. We produce experimental results comparing the GrAPFI-GO method with and without considering common neighbors similarity. We also test the performance of GrAPFI-GO and other annotation tools for GO annotation on a benchmark of proteins with and without the proposed pruning and post-processing procedure.ConclusionOur results show that the proposed semantic hierarchical post-processing potentially improves the performance of GrAPFI-GO and of other annotation tools as well. Thus, GrAPFI-GO exposes an original efficient and reusable procedure, to exploit the semantic relations among the GO terms in order to improve the automatic annotation of protein functions

Multi-scale local-temporal similarity fusion for continuous sign language recognition

Continuous sign language recognition (cSLR) is a public significant task that transcribes a sign language video into an ordered gloss sequence. It is important to capture the fine-grained gloss-level details, since there is no explicit alignment between sign video frames and the corresponding glosses. Among the past works, one promising way is to adopt a one-dimensional convolutional network (1D-CNN) to temporally fuse the sequential frames. However, CNNs are agnostic to similarity or dissimilarity, and thus are unable to capture local consistent semantics within temporally neighboring frames. To address the issue, we propose to adaptively fuse local features via temporal similarity for this task. Specifically, we devise a Multi-scale Local-Temporal Similarity Fusion Network (mLTSF-Net) as follows: 1) In terms of a specific video frame, we firstly select its similar neighbours with multi-scale receptive regions to accommodate different lengths of glosses. 2) To ensure temporal consistency, we then use position-aware convolution to temporally convolve each scale of selected frames. 3) To obtain a local-temporally enhanced frame-wise representation, we finally fuse the results of different scales using a content-dependent aggregator. We train our model in an end-to-end fashion, and the experimental results on RWTH-PHOENIX-Weather 2014 datasets (RWTH) demonstrate that our model achieves competitive performance compared with several state-of-the-art models.

Analysis of user electricity consumption behavior based on density peak clustering with shared neighbors and attractiveness

SummaryUser behavior analysis is the research foundation of power load forecasting and power abnormal detection, and it is the theoretical support for smart grid planning and the construction of energy internet. Aiming at the complex characteristics of high‐dimensional, noisy, and multi‐redundant of power load data, this article used the principal component analysis (PCA) to reduce the dimensionality of power data. The density peaks clustering algorithm with shared neighbor and attractiveness (DPC‐SNA) was then used to cluster the data with reduced dimensionality to extract the user's electricity consumption characteristics. The DPC‐SNA algorithm first constructs a sample similarity measurement criterion that shares the similarity of neighbors, on which the local density is defined accordingly. The new local density can effectively distinguish the contributions of local samples and global samples. Incorporating the idea of universal gravitation, a new calculation method of sample attractiveness was defined, and the remaining samples were allocated by the attractiveness matrix. Experiment was performed using the actual load data of special transformer users in a certain area. The results show that there were five typical electricity consumption behavior characteristics in this area, namely, the evening‐peak production type, all‐day production type, multi‐peak production type, daytime production type, and night production type. The corresponding load peak time periods were also obtained.

WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19.

The global coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV) has led to a huge health and economic crises. However, the research required to develop new drugs and vaccines is very expensive in terms of labor, money, and time. Owing to recent advances in data science, drug-repositioning technologies have become one of the most promising strategies available for developing effective treatment options. Using the previously reported human drug virus database (HDVD), we proposed a model to predict possible drug regimens based on a weighted reconstruction-based linear label propagation algorithm (WLLP). For the drug-virus association matrix, we used the weighted K-nearest known neighbors method for preprocessing and label propagation of the network based on the linear neighborhood similarity of drugs and viruses to obtain the final prediction results. In the framework of 10 times 10-fold cross-validated area under the receiver operating characteristic (ROC) curve (AUC), WLLP exhibited excellent performance with an AUC of 0.8828 ± 0.0037 and an area under the precision-recall curve of 0.5277 ± 0.0053, outperforming the other four models used for comparison. We also predicted effective drug regimens against SARS-CoV-2, and this case study showed that WLLP can be used to suggest potential drugs for the treatment of COVID-19.

A new energy-efficient and temperature-aware routing protocol based on fuzzy logic for multi-WBANs

Temperature-aware routing of the sensed data is one of the significant challenges in Wireless Body Area Networks (WBANs). This article presents a novel fuzzy logic-based and thermal-aware clustering and routing scheme for multi-WBANs that attempts to cluster similar sensors together and benefits from data aggregation. In this scheme, a Fuzzy Logic Controller (FLC) is applied for clustering that receives essential factors such as the temperature of CH, number of similar neighbors, number of neighbors, remaining energy, and path loss. Besides, in this scheme, the CHs can send their data to the coordinator of the other patients, and to do this, another FLC is presented, which receives crisp parameters such as the number of patients connected to the coordinator, packet delivery ratio, and distance. To improve the effectiveness of these FLCs, a new hybrid metaheuristic algorithm, named HAOA, is presented that tunes the FLCs’ parameters and reasoning rules. The HAOA algorithm was created by improving and combining the exploration and exploitation phases of the Aquila optimizer algorithm with the AOA algorithms, aiming to mitigate the local optima problem in AOA and its convergence speed. Extensive simulations were conducted in several realistic scenarios, and their results indicate that the proposed temperature-based clustering and routing method can prevent hotspot problems in WBAN while improving its stability and lifetime.

BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix.

With the high-quality development of bioinformatics technology, miRNA-disease associations (MDAs) are gradually being uncovered. At present, convenient and efficient prediction methods, which solve the problem of resource-consuming in traditional wet experiments, need to be further put forward. In this study, a space projection model based on block matrix is presented for predicting MDAs (BMPMDA). Specifically, two block matrices are first composed of the known association matrix and similarity to increase comprehensiveness. For the integrity of information in the heterogeneous network, matrix completion (MC) is utilized to mine potential MDAs. Considering the neighborhood information of data points, linear neighborhood similarity (LNS) is regarded as a measure of similarity. Next, LNS is projected onto the corresponding completed association matrix to derive the projection score. Finally, the AUC and AUPR values for BMPMDA reach 0.9691 and 0.6231, respectively. Additionally, the majority of novel MDAs in three disease cases are identified in existing databases and literature. It suggests that BMPMDA can serve as a reliable prediction model for biological research.

MSF-UBRW: An Improved Unbalanced Bi-Random Walk Method to Infer Human lncRNA-Disease Associations.

Long-non-coding RNA (lncRNA) is a transcription product that exerts its biological functions through a variety of mechanisms. The occurrence and development of a series of human diseases are closely related to abnormal expression levels of lncRNAs. Scientists have developed many computational models to identify the lncRNA-disease associations (LDAs). However, many potential LDAs are still unknown. In this paper, a novel method, namely MSF-UBRW (multiple similarities fusion based on unbalanced bi-random walk), is designed to explore new LDAs. First, two similarities (functional similarity and Gaussian Interaction Profile kernel similarity) of lncRNAs are calculated and fused linearly, also for disease data. Then, the known association matrix is preprocessed. Next, the linear neighbor similarities of lncRNAs and diseases are calculated, respectively. After that, the potential associations are predicted based on unbalanced bi-random walk. The fusion of multiple similarities improves the prediction performance of MSF-UBRW to a large extent. Finally, the prediction ability of the MSF-UBRW algorithm is measured by two statistical methods, leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The AUCs of 0.9391 in LOOCV and 0.9183 (±0.0054) in 5-fold CV confirmed the reliable prediction ability of the MSF-UBRW method. Case studies of three common diseases also show that the MSF-UBRW method can infer new LDAs effectively.

QoS prediction for web services in cloud environments based on swarm intelligence search

In the real-world cloud computing scenarios, a user usually invokes few Web services and can provide only a small proportion of QoS (Quality of Service) records. Therefore, for a whole range of services, many records for this user are missing (i.e. incomplete QoS information). These missing QoS values make it difficult to conduct accurate service recommendations. To overcome this difficulty, it is necessary to predict the missing QoS values for the user. Most existing algorithms tackle the QoS prediction problem through aggregating the QoS values of the local similar neighbors of the user, which easily leads to local optimization. In this paper, we model it as a global search optimization problem in the distribution space of QoS values and propose a novel algorithm PSO-USRec. In the algorithm, particle swarm optimization (PSO) is customized and improved by diversifying the initial solutions and smoothing the outlier particles. To validate the effectiveness of the PSO-USRec algorithm, comparison experiments are conducted on a well-known public QoS dataset. The experimental results show that the PSO-USRec algorithm significantly outperforms the state-of-the-art collaborative filtering approaches. It reduces MAE and RMSE by at least 5.42% and 1%, and at most 14.29% and 2.25%, respectively.

Contrastive graph neural network-based camouflaged fraud detector

Recently GNN-based graph machine learning methods have been widely used in fraud detection. Prior works have revealed the problems of camouflaged fraudsters on graphs, which dilute the suspicion of aggregated features of fraudsters. The fraudsters’ camouflage lies in two main aspects: 1) fraudsters adjust their attributes to camouflage original hand-craft features against detection. 2) fraudsters connect to benign entities, making the labels of their contexts inconsistent. However, previous neighbor sampling methods based on similarity measures are inadequate in calculating neighbor similarity, resulting in a loss of GNN-based detector performance. In this paper, we enhance the node similarity measure-based neighbor sampling method and the multi-relational aggregator. First, we use supervised contrastive learning to aggregate discriminative node embeddings. After that, the consistent neighbors are sampled according to the contrastive embedding similarity as well as the node feature similarity. Finally, we improve the multi-relational aggregator via contrastive embeddings. Experiments on two real-world datasets demonstrate that our model CACO-GNN achieves the highest scores in F1-macro, AUC, and Recall, with an 18.5% improvement in F1-macro performance, enabling more effective detection of camouflaged fraudsters.

Identifying accurate link predictors based on assortativity of complex networks

Link prediction algorithms in complex networks, such as social networks, biological networks, drug-drug interactions, communication networks, and so on, assign scores to predict potential links between two nodes. Link prediction (LP) enables researchers to learn unknown, new as well as future interactions among the entities being modeled in the complex networks. In addition to measures like degree distribution, clustering coefficient, centrality, etc., another metric to characterize structural properties is network assortativity which measures the tendency of nodes to connect with similar nodes. In this paper, we explore metrics that effectively predict the links based on the assortativity profiles of the complex networks. To this end, we first propose an approach that generates networks of varying assortativity levels and utilize three sets of link prediction models combining the similarity of neighborhoods and preferential attachment. We carry out experiments to study the LP accuracy (measured in terms of area under the precision-recall curve) of the link predictors individually and in combination with other baseline measures. Our analysis shows that link prediction models that explore a large neighborhood around nodes of interest, such as CH2-L2 and CH2-L3, perform consistently for assortative as well as disassortative networks. While common neighbor-based local measures are effective for assortative networks, our proposed combination of common neighbors with node degree is a good choice for the LP metric in disassortative networks. We discuss how this analysis helps achieve the best-parameterized combination of link prediction models and its significance in the context of link prediction from incomplete social and biological network data.

A reputation assessment model for trustful service recommendation

Nowadays, software systems are mainly Web front-based, Cloud-deployed and accessible by a wide audience over the Internet. These online systems commonly rely on Service-oriented Architecture principles, where they are built as orchestrations of RESTful (and in some rare cases as SOAP-based) services. Integrating new services in an existing orchestration is a challenging and risky task because trustworthiness of these services is not guaranteed throughout their lifetime. Reputation of services is a good indicator about the overall quality of services, because it reflects consumer satisfaction regarding the service-offered functionality and quality. Thus, reputation of services could be considered in the selection and recommendation of trustworthy services. In this paper, we present a framework for the management of web service reputation to conduct a better service recommendation. We present a reputation assessment model that aggregates fair user feedback ratings. The model includes a mechanism that prevents the introduction of malicious feedback ratings, by penalizing detected specious users. In addition, this framework includes a bootstrapping technique for estimating reputation of newcomer services based on neighbor similarity and initial advertised QoS. A set of experiments has been conducted to evaluate the effectiveness of the proposed framework. The results of these experiments highlighted the potential of our framework. These are presented at the end of the paper.

An Improved Metalearning Framework to Optimize Bearing Fault Diagnosis under Data Imbalance

The intelligent diagnosis of rotating machinery with big data has been widely studied. However, due to the variability of working conditions and difficulty in marking fault samples, it is difficult to obtain enough high-quality fault marking data for training bearing fault diagnosis models in practical industrial application scenarios. Aiming at the problem of training data imbalance caused by lack of fault samples, a novel metalearning fault diagnosis method (MOFD) is proposed to get the bearing fault diagnosis solution under data imbalance. Firstly, in order to enhance the variety of fault samples, a Feature Space Density Adaptive Synthetic Minority Oversampling Technique (FSDA-SMOTE) is proposed in this paper, which takes the density difference of minority samples in the spatial domain within the class as the constraint of local neighbor similarity to generate new fault samples for data augmentation. In addition, in order to strengthen the model’s learning ability and diagnosis performance under limited fault samples, a residual-attention convolutional neural network (RA-CNN) was constructed to identify the deep features of fault signals, and a metalearning strategy based on parameter gradient optimization was applied to RA-CNN for refining the learning process of the diagnosis model. Finally, the reliability of the proposed method is verified through experimental analysis of public bearing dataset.

An improved decision tree algorithm based on variable precision neighborhood similarity

The decision tree algorithm has been widely used in data mining and machine learning due to its high accuracy, low computational cost and high interpretability. However, when dealing with the continuous data, the classical decision tree algorithm needs to replace continuous attributes with discretized attributes by the strategy of discretization. Discretization may cause a loss of information structure, which will affect the performance of classification. To tackle this problem, many researchers have proposed different decision tree methods based on variable precision neighborhood rough sets. However, these methods do not consider the geometric structure of neighborhood systems, which may lead to a contradiction in the transitivity of the equivalence relation. In this paper, we first define a novel neighborhood geometric similarity in a neighborhood system from the perspective of geometry. Second, by combining the neighborhood geometric similarity and the neighborhood algebraic similarity, we propose four new kinds of neighborhood similarities, which can solve the contradictory transitivity of the equivalence relation. Third, a variable precision neighborhood rough set model is constructed using the new similarities, and a novel decision tree algorithm is proposed based on this model, where the degree of attribute dependence is used as the partition measure. Experimental results on 14 selected datasets from the UCI Machine Learning Repository show that our algorithm is effective. The average accuracy of our algorithm is over 90%, which is 10% higher than the classical decision tree algorithms, and the number of leaf nodes increases slightly.

Dissimilarity effects on house prices: what is the value of similar neighbours?

Residential choice does not only depend on properties of the dwelling, neighbourhood amenities and affordability, but is also affected by the population composition within a neighbourhood. All these attributes are capitalised in the house price. Empirically, it is not easy to disentangle the effect of the neighbourhood on house prices from the effects of the dwelling attributes. We implement an agent-based model of an urban housing market that allows us to analyse the interaction between residential choice, population composition in a neighbourhood and house prices. Agents differ in terms of education, income and group affiliation (majority vs. minority). Whereas rich agents can afford to move to preferred places, roughly 13.01% of poor minorities and 8.02% of poor majority agents are locked in their current neighbourhood. We show that a preference to live among similar neighbours has a strong competitive effect on rich households and drives up their house prices. This is not the case with a preference for status. By introducing a policy that provides agents more access to credit, we find that all population groups denote higher satisfaction levels. Poor agents show the largest improvements. The general satisfaction level across all population groups increases. However, the extra credit accessibility also drives up house prices and leads to higher wealth inequality within the city. If agents have a preference for status rather than for similarity, the effect of the overall inequality is smaller, since agents become more satisfied living in areas with less similar agents.

Case Based Reasoning Expert System Identification of Top Brand Merchant Online Food PT. Solusi Transportasi Indonesia

PT. Solusi Transportasi Indonesia or what we know better as grab, at this time not only engaged in people delivery services, but also other features, one of which is grabfood. Grabfood cooperates with the best restaurants we call merchants in the Greater Jakarta area, Medan, Surabaya, Bandung, Bali, Makassar, Palembang, and several other cities in Indonesia. Grabfood uses 2 payment methods, cash and non-cash. To achieve this goal of developing grabfood services, periodic identification of merchants who are top brands is carried out in order to provide services and increase customer satisfaction. Several things can be done due to the rapid development of technology and information, one of the things that can be done is to identify the Top Brand. One of the easy ways to identify it is to use an expert system. This expert system was created to be used by companies in identifying Top Brand Merchant. Data collection techniques were carried out by conducting interviews with related companies and browsing the internet. The inference method used in the development of this expert system is the case base reasoning method, which is a method that draws conclusions from new cases based on old cases and provides solutions to new cases based on old cases that have the highest similarity value. Cases that commonly occur and are encountered can be used as a reference to identify Top Brand Merchant when there are new cases. The use of cases that have occurred previously or old cases is generally known as case-based reasoning (CBR). This study discusses an expert system to identify Top Brand Merchant using CBR reasoning method with nearest neighbor similarity measurement method. Keywords: Expert System, Case Base Reasoning, Neighbor Similarity, Top Brand Merchant