scholarly journals An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases

2021 ◽  
Vol 13 (6) ◽  
pp. 70-82
Author(s):  
K Laskhmaiah ◽  
◽  
S Murali Krishna ◽  
B Eswara Reddy
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Spatial Databases ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Rand Index ◽  
Spatial Distance ◽  
Experimental Result ◽  
Adjusted Rand Index ◽  
Second Phase

From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but nonoverlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With nonoverlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized KMeans with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.

Study on Fuzzy Clustering Algorithm of Spatial Data Mining

2013 ◽  
Vol 416-417 ◽  
pp. 1244-1250
Author(s):  
Ting Ting Zhao
Keyword(s):  
Data Mining ◽  
Fuzzy Clustering ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Rapid Development ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Data Set ◽  
Fuzzy Similarity

With rapid development of space information crawl technology, different types of spatial database and data size of spatial database increases continuously. How to extract valuable information from complicated spatial data has become an urgent issue. Spatial data mining provides a new thought for solving the problem. The paper introduces fuzzy clustering into spatial data clustering field, studies the method that fuzzy set theory is applied to spatial data mining, proposes spatial clustering algorithm based on fuzzy similar matrix, fuzzy similarity clustering algorithm. The algorithm not only can solve the disadvantage that fuzzy clustering cant process large data set, but also can give similarity measurement between objects.

Spatial Data Mining Based on Campus GIS

2011 ◽  
Vol 282-283 ◽  
pp. 641-645 ◽  
Author(s):  
Yong Ling Yu ◽  
Tao Guan ◽  
Jin Fa Shi
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Databases ◽  
Rapid Development ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Prototype System ◽  
Spatial Features ◽  
Database Technology ◽  
Mining Model

With the rapid development of spatial database technology, spatial databases have been widely used in many engineering fields and rapidly increase in data capacity. Thus, to mine useful information from large spatial databases turns into a difficult but important task. In this paper, we apply the traditional data mining into spatial database and give a mining model for spatial data based on Campus GIS. Moreover, based on campus GIS, we implement a spatial data mining prototype system that is able to discovery the useful spatial features and patterns in spatial databases. The application in the campus GIS of a university has shown the feasibility and validity of the system.

A Survey of Spatial Data Mining Methods Databases and Statistics Point of Views

2011 ◽  
pp. 229-242 ◽  
Author(s):  
Karine Zeitouni
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Spatial Statistics ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Mining Methods

This chapter reviews the data mining methods that are combined with Geographic Information Systems (GIS) for carrying out spatial analysis of geographic data. We will first look at data mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the research that is currently going on in this area, pointing out that there are two approaches: the first comes from learning on spatial databases, while the second is based on spatial statistics. We will conclude by discussing the main differences between these two approaches and the elements they have in common.

scholarly journals Clustering for Probability Density Functions by New k-Medoids Method

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
D. Ho-Kieu ◽  
T. Vo-Van ◽  
T. Nguyen-Trang
Keyword(s):  
Probability Density ◽  
Clustering Algorithm ◽  
Real Life ◽  
Probability Density Functions ◽  
Rand Index ◽  
Computational Time ◽  
Adjusted Rand Index ◽  
Density Functions ◽  
Potential Applications ◽  
Iteration Number

This paper proposes a novel and efficient clustering algorithm for probability density functions based on k-medoids. Further, a scheme used for selecting the powerful initial medoids is suggested, which speeds up the computational time significantly. Also, a general proof for convergence of the proposed algorithm is presented. The effectiveness and feasibility of the proposed algorithm are verified and compared with various existing algorithms through both artificial and real datasets in terms of adjusted Rand index, computational time, and iteration number. The numerical results reveal an outstanding performance of the proposed algorithm as well as its potential applications in real life.

Geographic Knowledge Discovery in Multiple Spatial Databases

2017 ◽  
pp. 344-366 ◽  
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

scholarly journals Research on the Parallelization of the DBSCAN Clustering Algorithm for Spatial Data Mining Based on the Spark Platform

2017 ◽  
Vol 9 (12) ◽  
pp. 1301 ◽  
Author(s):  
Fang Huang ◽  
Qiang Zhu ◽  
Ji Zhou ◽  
Jian Tao ◽  
Xiaocheng Zhou ◽  
...  
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Data Mining ◽  
Dbscan Clustering

scholarly journals MINING CO-LOCATION PATTERNS WITH CLUSTERING ITEMS FROM SPATIAL DATA SETS

2018 ◽  
Vol XLII-3 ◽  
pp. 2505-2509
Author(s):  
G. Zhou ◽  
Q. Li ◽  
G. Deng ◽  
T. Yue ◽  
X. Zhou
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Location Pattern ◽  
Spatial Feature ◽  
Geographic Space ◽  
Location Patterns ◽  
Spatial Data Sets

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

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