CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree
S. Sharmiladevi1, S. Siva Sathya2, Naveen Kumar3 

1S. Sharmiladevi, Department of Computer Science, Pondicherry University, Puducherry, India.
2S. Siva Sathya, Department of Computer Science, Pondicherry University, Puducherry, India.
3Naveen Kumar, Department of Computer Science and Engineering, KL University, Guntur, India.

Manuscript received on 03 March 2019 | Revised Manuscript received on 08 March 2019 | Manuscript published on 30 July 2019 | PP: 1164-1171 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1741078219/19©BEIESP | DOI: 10.35940/ijrte.B1741.078219
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Abstract: Data about entities or objects associated with geographical or location information could be called as spatial data. Spatial data helps in identifying and positioning anyone or anything globally anywhere across the world. Instances of various spatial features that are closely found together are called as spatial co-located patterns. So far, the spatial co-located patterns have been used only for knowledge discovery process but it would serve a wide variety of applications if analyzed intensively. One such application is to use co-location pattern mining for a context aware based search. Hence the main aim of this work is to extend the K-Nearest Neighbor (KNN) querying to co-located instances for context aware based querying or location-based services (LBS). For the above-said purpose, co-located nearest neighbor search algorithm namely “CONNEKT” is proposed. The co-located instances are mapped onto a K-dimensional tree (K-d tree) inorder to make the querying process efficient. The algorithm is analyzed using a hypothetical data set generated through QGIS.
Index Terms: Co-located Pattern Mining, K-Nearest Neighbor, K-Dimensional Tree, Location-Based Services.

Scope of the Article: Web Mining