Analysis of Similarity Measures with WordNet Based Text Document Clustering

Sandhya, N.; Govardhan, A.

doi:10.1007/978-3-642-27443-5_80

Cited by 20 publications

(17 citation statements)

References 7 publications

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“…We use the Pearson distance (a transform of the Pearson correlation [21]), as opposed to the many other distance measures [1,2,20,21], as the Pearson distance often produces a clustering that is closer to the true clustering (i.e., a closer match to the manually assigned clusters) [25,40]. We find that the Pearson distance performs well when clustering system signatures (see Section 5).…”

Section: Distance Calculationmentioning

confidence: 95%

Continuous validation of load test suites

Syer

Jiang

Nagappan

et al. 2014

Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering

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Ultra-Large-Scale (ULS) systems face continuously evolving field workloads in terms of activated/disabled feature sets, varying usage patterns and changing deployment configurations. These evolving workloads often have a large impact on the performance of a ULS system. Hence, continuous load testing is critical to ensuring the error-free operation of such systems. A common challenge facing performance analysts is to validate if a load test closely resembles the current field workloads. Such validation may be performed by comparing execution logs from the load test and the field. However, the size and unstructured nature of execution logs makes such a comparison unfeasible without automated support. In this paper, we propose an automated approach to validate whether a load test resembles the field workload and, if not, determines how they differ by compare execution logs from a load test and the field. Performance analysts can then update their load test cases to eliminate such differences, hence creating more realistic load test cases. We perform three case studies on two large systems: one open-source system and one enterprise system. Our approach identifies differences between load tests and the field with a precision of ≥75% compared to only ≥16% for the state-of-the-practice.

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Section: Distance Calculationmentioning

confidence: 95%

Continuous validation of load test suites

Syer

Jiang

Nagappan

et al. 2014

Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering

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“…as an intersection of an object that is divided by union of the object (Sandhya, et al, 2008;Leskovec, Rajaraman, & Ullman, 2011). Jaccard Similarity of the set S and T are JS (S, T), with the following equation…”

Section: Association Rule Miningmentioning

confidence: 99%

“…To calculate the value of similarity, this research used Jaccard Similarity because euclidean distance is a measure of the distance, whereas the cosine similarity, the Jaccard coefficient and the Pearson coefficient is a measure of similarity (Sandhya, et al, 2008) and also because Jaccard Similarity is suitable for a wide range of applications, such as textual similarity or similarity of the buying habits of customers (Leskovec, Rajaraman, & Ullman, 2011). The end result of this detection application is the trustines value of reviewers, the honesty value of reviews, and the reliability value of a product.…”

Section: Introductionmentioning

confidence: 99%

Fake Review Detection From a Product Review Using Modified Method of Iterative Computation Framework

Wahyuni

Djunaidy

2016

MATEC Web of Conferences

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The rapid growth of the Internet influenced many of our daily activities. One of the very rapid growth area is ecommerce. Generally e-commerce provide facility for customers to write reviews related with its service. The existence of these reviews can be used as a source of information. For examples, companies can use it to make design decisions of their products or services, while potential customers can use it to decide either to buy or to use a product. Unfortunately, the importance of the review is misused by certain parties who tried to create fake reviews, both aimed at raising the popularity or to discredit the product. This research aims to detect fake reviews for a product by using the text and rating property from a review. In short, the proposed system (ICF++) will measure the honesty value of a review, the trustiness value of the reviewers and the reliability value of a product. The honesty value of a review will be measured by utilizing the text mining and opinion mining techniques. The result from the experiment shows that the proposed system has a better accuracy compared with the result from iterative computation framework (ICF) method.

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“…The Euclidean distance has been used in a variety of studies [5], [11] as a basis for comparison as well as simple metric.…”

Section: A Euclidean Distancementioning

confidence: 99%

“…The most notable of these are the Euclidean distance, cosine similarity, Pearson correlation coefficient, Kullback-Leibler divergence and extended Jaccard coefficient, which have all been analysed in comparative studies [5], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Construction of Topic-Based Twitter Lists

Villiers

Hoffmann

Kroon

2012

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing

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The Twitter lists feature was launched in late 2009 and enables the creation of curated groups containing Twitter users. Each user can be a list author and decide the basis on which other users are added to a list. The most popular lists are those that associate with a topic. Twitter lists can be used as a powerful organisation tool, but its widespread adoption has been limited. The two main obstacles are the initial setup time and the effort of continual curation. In this paper we attempt to solve the first problem by applying unsupervised clustering algorithms to construct topic-based Twitter lists. We consider k-means and affinity propagation (AP) as clustering algorithms and evaluate these algorithms using two document representation techniques. The selected representation techniques are the popular term frequency-inverse document frequency (TF-IDF) and the latent Dirichlet allocation (LDA) topic model. We calculate the similarities for the clustering algorithms using five well-known similarity measures that have been used extensively in the text domain. The adjusted normalised information distance (ANID) was used to compare the clustering result yielded by k-means and affinity propagation. We found that the careful selection of a similarity measure, combined with the LDA topic model can provide a user with a sensible starting point for list creation.

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Analysis of Similarity Measures with WordNet Based Text Document Clustering

Cited by 20 publications

References 7 publications

Continuous validation of load test suites

Continuous validation of load test suites

Fake Review Detection From a Product Review Using Modified Method of Iterative Computation Framework

Unsupervised Construction of Topic-Based Twitter Lists

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