Clustering spatial data with a geographic constraint: exploring local search

Abstract

Spatial data objects that possess attributes in the optimization domain and the geographic domain are now widely available. For example, sensor data are one kind of spatial data objects. The location of a sensor is an attribute in the geographic domain, while its reading is an attribute in the optimization domain. Previous studies discuss dual clustering problems that attempt to partition spatial data objects into several groups, such that objects in the same group have similar values in their optimization attributes and form a compact region in the geographic domain. However, previous studies do not clearly define compact regions. Therefore, this paper formulates a connective dual clustering problem with an explicit connected constraint given. Objects with a geographic distance smaller than or equal to the connected constraint are connected. The goal of the connective dual clustering problem is to derive clusters that contain objects with similar values in the optimization domain and are connected in the geographic domain. This study further proposes an algorithm CLS (Clustering with Local Search) to efficiently derive clusters. This algorithm consists of two phases: the ConGraph (standing for Connective Graph) transformation phase and the clustering phase. In the ConGraph transformation phase, CLS first transforms the data objects into a ConGraph that captures geographic constraints among data objects and selects initial seeds for clustering. Then, the initial seeds selected nearby data objects and formed coarse clusters by exploring local search in the clustering phase. Moreover, coarse clusters are merged and finely turned. Experiments show that CLS algorithm is more efficient and scalable than existing methods.

A Survey of Context-Aware Mobile Recommendations

“…The e®ectiveness of context-aware web search has been proved before, 22 and more contextual information such as location has been exploited for better clustering 72 and personalization. 17 Church and Smyth 28 proposed a novel interface to support multidimensional, context-sensitive mobile search, combining context features such as location, time, and community preferences to o®er a unique search experience that is well adapted to the needs of mobile users.…”

A multi-layer Internet of things database schema for online-to-offline systems

“…Zhang and Li 15 proposed a grid-based geography data engine to search spatial data in a distributed environment. Both Liao and Peng 16 and Lu et al 17 studied the methods of synchronously accessing geography information and content information. In Liao and Peng, 16 a cluster method is proposed to process contexts with specific location restrictions.…”

“…Both Liao and Peng 16 and Lu et al 17 studied the methods of synchronously accessing geography information and content information. In Liao and Peng, 16 a cluster method is proposed to process contexts with specific location restrictions. In Lu et al, 17 a hybrid index tree named Intersection-Union R-tree (IUR-tree) is designed to compute content similarity.…”

Mining Graph Topological Patterns: Finding Covariations among Vertex Descriptors

“…neither the relationships can be derived from the feature vectors nor vice versa". Attributed graphs are extensively studied by means of clustering techniques (see e.g., [1], [8], [13], [14], [20], [35])…”