FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

Abstract

With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data.

Mining significant local spatial association rules for multi-category point data

“…Common local methods can be divided into methods based on frequent item sets and methods based on statistics. In the study of methods based on frequent item sets, Jang et al proposed a region-based frequent-rule growth algorithm to search for association rules in dense areas and discovered spatial association rules in local areas [38] . In the study of methods based on statistics.…”

Edge Computing for Internet of Things

“…IoT data vary depending on their deployment and application; all data need to be processed in some way. In [9], the authors propose algorithms for processing spacial data that are of interest to the data mining field. They propose region-based frequent pattern growth (RFP-Growth) to search for associations in IoT data.…”

Identifikasi Pola Penjualan Barang dalam Menjaga Stabilitas Stok Menggunakan Algoritma Fp- Growth

“…Item ini mewakili konsolidasi dataset yang terjadi bersama dalam data dengan beberapa asosiasi antara atributnya. Algoritma ini sebenarnya membangun pohon, yang membantu mengidentifikasi hubungan antar item Fp-Growth penting untuk menetapkan nilai dukungan minimum yang wajar, pertumbuhan FP yang mencakup dua fase, membangun pohon FP dan menambang pohon FP [9]. Tahapan algoritma FP-Growth dapat dilihat pada Gambar 1 Gambar 1.…”