Spatial Data Mining Methods Databases and Statistics Point of Views

Flora D. Salim Amr Mohamed A. Erradi + 3 lainnya

31 Maret 2021

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

Zhe Jiang Arun Sharma S. Shekhar

26 Juni 2022

Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.

Dr. S. Vijayalakshmi K. K. Nivethithaa

1 Juni 2021

The term “Data Mining” is refers to the extraction of patterns and knowledge from large amounts of raw data and often defined as finding hidden information in a database. It insinuate analyzing data patterns in large volume of data using one or more software. Data mining involves effective data collection and warehousing as well as computer processing.

F. Kasmin S. K. Baharin E. Ernawati

1 April 2021

Data mining (DM) is the process of extracting knowledge from data. Knowledge from customer behaviour segmentation is useful for companies in setting the target market and developing a marketing strategy. Recency Frequency Monetary (RFM) model is the most behaviour segmentation used. Many customer-segmentation studies in various application areas use the RFM model that collaborates with DM. With many methods in DM, the selection of appropriate methods can reveal useful hidden patterns in customer segments. This paper aims to analyse DM methods that collaborate with the RFM model and synthesize them to propose a customer segmentation framework. This study uses a comprehensive literature review published in 2015-2020. The most widely used methods are clustering and visualization from seven DM methods analysed. Due to the increased visualization function and the need for customers’ geo-demographic data to be considered in the analysis, this study presents a new framework for using DM methods with the RFM based segmentation in the Geographic Information Systems (GIS) environment. This framework helps analysts utilize DM methods to uncover and understand customer characteristics, so companies can set the target market and develop a marketing strategy to increase their competitive advantage.

Débora Libertad Ramírez Vargas Imran Shafi Mahnoor Mahnoor + 3 lainnya

31 Agustus 2023

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.