Density Based Clustering over Location Based Services

Rahman, Farhadur; Liu, Weimo; Suhaim, Saad Bin; Thirumuruganathan, Saravanan; Zhang, Nan; Das, Gautam

doi:10.1109/icde.2017.103

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…To reduce the dimensionality we have applied the Uniform Manifold Approximation and Projection (UMAP) algorithm [52] as it allows to preserve the high-dimensional local structure in lower dimensional space. For clustering we have implemented the Hierarchical density based clustering (HDBSCAN) [53], which transform the vector space according to the density/sparsity, construct cluster hierarchy of connected components and extracts the stable clusters from the condensed tree. The parameters we fine tuned for this experiment are:…”

Section: Topic Modeling With Bertmentioning

confidence: 99%

Analyzing Deceptive Opinion Spam Patterns: the Topic Modeling Approach

Jakupov

Mercadal

Zeddini

et al. 2022

2022 IEEE 34th International Conference on Tools With Artificial Intelligence (ICTAI)

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Deceptive Opinion Spam commonly takes the form of fake reviews (negative or positive) posted by a malicious web user to hurt or inflate a company's image. As these reviews have been deliberately written to deceive the reader, human reviewers are faring little better than a chance in detecting these deceptive statements. Thus, there is a dire need to address this issue as extracting text patterns from the fraudulent texts with meaningful substructures still remains a challenge. In our research, to obtain a deeper understanding of how lies are expressed in texts, we consider the task as a topic modeling problem, in which we constructed a model to learn the patterns that constitute a fake review, and then explore the outputs of this model to identify those patterns. Topic models may be useful in this task due to their ability to group multiple documents into smaller sets of key topics. As the linguistic cues of the lies are still unknown, a key advantage of this approach is that the algorithm encourages the mixtures composed of only few topics, which makes the representation more interpretable and provides additional opportunities to reveal the patterns and structures within the systems of documents. Our methodology proved to be useful for this study, revealing the lexical cues generally applied by human reviewers to generate deceptive language.

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Section: Topic Modeling With Bertmentioning

confidence: 99%

Analyzing Deceptive Opinion Spam Patterns: the Topic Modeling Approach

Jakupov

Mercadal

Zeddini

et al. 2022

2022 IEEE 34th International Conference on Tools With Artificial Intelligence (ICTAI)

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“…A density based clustering algorithm for location based services is proposed in [10]. This approach clusters the nearby locations with respect to a query location and returns the user with a set nearby points.…”

Section: Review Of Literaturementioning

confidence: 99%

A Novel Density Based Clustering Algorithm by Incorporating Mahalanobis Distance

Sangeetha¹,

Padikkaramu²,

Chellan³

2018

IJIES

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Data clustering is one of the active research areas, which aims to group related data together. The process of data clustering improves the data organization and enhances the user experience as well. For this sake, several clustering algorithms are proposed in the literature. However, a constant demand for a better clustering algorithm is still a basic requirement. Understanding the necessity, this paper proposes a density based clustering algorithm which is based on Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The main drawback of DBSCAN algorithm is it requires two important parameters as initial input. It is really difficult to fix the values for these parameters, as it requires some prior knowledge about the dataset. This requirement is overthrown by the proposed clustering algorithm by selecting the parameters automatically. The automated selection of parameters is achieved by analysing the dataset and it varies from dataset to dataset. This way of parameter selection improves the quality of service and produce effective clusters. The experimental results show that the proposed approach outperforms the DBSCAN algorithm in terms of purity, F-measure and entropy.

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“…From the data sets crawled from a well-known social media platform Dianping in China, we find some insightful correlations between expenditures and rating scores: 1) transactions or experiences with higher expenditures usually lead to higher rating scores; 2) when the real expenditures are higher than users' normal spending behavior, the users usually give higher scores; and 3) there are multiple grades of expenditure behaviors. Jianxun Liu et al [9], proposes a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service.…”

Section: Literature Surveymentioning

confidence: 99%

Geographical Location based Service Recommendation

Choubey¹

2018

IJRASET

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Food recommendation system is an extremely well known service whose precision and advancement continues expanding each day. With the coming of cell phones and web technologies like 3G or 4G, this has turned out to be available by each purchaser. In this paper, we propose a recommendation system which can able to find out best food for users based on the location of that person. Proposed method has various phases such as, feature extraction, feature vectorization and finding cosine similarities for each of the users to best match their tastes.

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Density Based Clustering over Location Based Services

Cited by 8 publications

References 12 publications

Analyzing Deceptive Opinion Spam Patterns: the Topic Modeling Approach

Analyzing Deceptive Opinion Spam Patterns: the Topic Modeling Approach

A Novel Density Based Clustering Algorithm by Incorporating Mahalanobis Distance

Geographical Location based Service Recommendation

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