Short text clustering using numerical data based on n-gram

Kumar, Rajiv; Mathur, Robin Prakash

doi:10.1109/confluence.2014.6949257

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…10 user profile attributes are considered in this approach, as shown in Table 1. The N-gram similarity [39], Levenshtein Distance [40], Jaccard distance [41], etc., some general proximity measures are much useful for comparing profile characteristics such as company name, location, and role title names. A loop iterates on the entire dataset in the proposed technique and clusters them together according to the stated threshold value specified using the metric of similarity, and data is saved as a matrix.…”

Section: Resultsmentioning

confidence: 99%

Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Kodabagi

2022

MJCS

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The establishment of connections among social network users using their profile information is an important task in social network analysis, which facilitates the development of various technological solutions such as stock market analysis, crime detection, tracking system of fraudulent events, etc. In this work, a proximity-based clustering method for networking LinkedIn profiles is presented. The proposed system computes proximity value between users using various attributes of user profiles. The proximity measures are computed by analyzing unstructured data of user profiles to connect users. The method addresses various issues such as comparison of familiar sentences, finding patterns, and sub-patterns among user profiles, assigning weights on attributes similarity, and computing total similarity which is associated with unstructured data. After computing proximity measures on various attributes of user profiles, the connecting edges between nodes are determined by employing artificial intelligence and a network graph is formed. The method is evaluated on a LinkedIn data-set to form a connected graph. The strength of the proposed methodology lies in the formation of multi-layered network graphs, as it uses various attributes of the user profiles to connect them. The proposed methodology helps various applications like recommendation systems to form network graphs of selected attributes and perform the social network analysis. The method achieves an accuracy of 96%. However, the profiles containing abbreviations of important information are not matched and the system accuracy drops down in such cases.

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Section: Resultsmentioning

confidence: 99%

Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Kodabagi

2022

MJCS

View full text Add to dashboard Cite

show abstract

Short text clustering using numerical data based on n-gram

Cited by 1 publication

References 6 publications

Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Connecting User Profiles of Social Networks Using Proximity-Based Clustering

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