Knowledge Discovery by Mining Association Rules and Temporal-Spatial Information from Large-Scale Geospatial Image Databases

2006 ◽  
Author(s):  
C.-R. Shyu ◽  
M. Klaric ◽  
G. Scott ◽  
W. Mahamaneerat
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
Large Scale ◽  
Spatial Information ◽  
Image Databases ◽  
Mining Association Rules

A Novel Efficient Mining Association Rules Algorithm for Distributed Databases

2010 ◽  
Vol 108-111 ◽  
pp. 50-56 ◽  
Author(s):  
Liang Zhong Shen
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
New Method ◽  
Rule Mining ◽  
Mining Association Rules

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate professionals, association rule mining is receiving increasing attention. The technology of data mining is applied in analyzing data in databases. This paper puts forward a new method which is suit to design the distributed databases.

MINING ASSOCIATION RULES FROM MARKET BASKET DATA USING SHARE MEASURES AND CHARACTERIZED ITEMSETS

1998 ◽  
Vol 07 (02) ◽  
pp. 189-220 ◽  
Author(s):  
ROBERT J. HILDERMAN ◽  
HOWARD J. HAMILTON ◽  
COLIN L. CARTER ◽  
NICK CERCONE
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
A Priori ◽  
Experimental Results ◽  
Market Basket ◽  
Large Databases ◽  
Mining Association Rules ◽  
Customer Profiles ◽  
Concept Hierarchies

We propose the share-confidence framework for knowledge discovery from databases which addresses the problem of mining characterized association rules from market basket data (i.e., itemsets). Our goal is to not only discover the buying patterns of customers, but also to discover customer profiles by partitioning customers into distinct classes. We present a new algorithm for classifying itemsets based upon characteristic attributes extracted from census or lifestyle data. Our algorithm combines the A priori algorithm for discovering association rules between items in large databases, and the A O G algorithm for attribute-oriented generalization in large databases. We show how characterized itemsets can be generalized according to concept hierarchies associated with the characteristic attributes. Finally, we present experimental results that demonstrate the utility of the share-confidence framework.

scholarly journals Analisa Pola Belanja Swalayan Daily Mart Untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Kezia Sumangkut ◽  
Arie S.M. Lumenta ◽  
Virginia Tulenan
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Knowledge Discovery In Database ◽  
Mining Association Rules

Abstrak --- Perkembangan pasar modern yang semakin hari semakin pesat dapat dilihat dari pusat perbelanjaan seperti supermarket, minimarket, grosir, dan lain sebagainya yang dibangun untuk kebutuhan melayani konsumen. Dan pemanfaatan data transaksi yang banyak dapat memberikan pengetahuan yang menarik dalam membuat kebijakan dan strategi penempatan rak barang. Maraknya perbelanjaan modern dan pesaing bisnis seperti itu tidak lepas dari peralihan pola pikir konsumen yang tadinya mencari harga yang murah, kini sudah memperhatikan aspek keamanan, kebersihan, kenyamanan, keramahan dalam pelayanan serta kelengkapan jenis barang dan penempatan rak barang. Oleh karena itu dalam penelitian ini, penulis mengangkat permasalahan tentang Analisa Pola Belanja Swalayan Daily Mart Untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth, dalam pelayanan yang sering terjadi di swalayan Daily Mart, dan  untuk mewujudkan hal itu penulis menerapkan metodologi KDD (Knowledge Discovery in Database). Salah satu teknik Data Mining dalam penelitian ini adalah Association Rule dalam Java Weka untuk mencari pengetahuan pola dari pembelian konsumen. Hasil dari penelitian ini berupa data pola pembelian/struk yang memiliki nilai confidence yang tinggi sebagai bahan untuk merekomendasi tata letak sesuai banyak barang yang paling sering dibeli. Kata Kunci --- Data Mining, Association Rules, Market Based Analysis, Java Weka

Out in the wash: spatial ecology of a temperate marine shallow rocky-reef species derived using acoustic telemetry

2015 ◽  
Vol 66 (6) ◽  
pp. 559 ◽  
Author(s):  
Jerom R. Stocks ◽  
Charles A. Gray ◽  
Matthew D. Taylor
Keyword(s):  
Spatial Ecology ◽  
Large Scale ◽  
Spatial Information ◽  
Activity Space ◽  
Rocky Reef ◽  
Temperate Reef ◽  
Detection Frequency ◽  
Detection Depth ◽  
Eastern Australia ◽  
Temperate Reef Fish

Characterising the movement and habitat affinities of fish is a fundamental component in understanding the functioning of marine ecosystems. A comprehensive array of acoustic receivers was deployed at two near-shore coastal sites in south-eastern Australia, to examine the movements, activity-space size and residency of a temperate rocky-reef, herbivorous species Girella elevata. Twenty-four G. elevata individuals were internally tagged with pressure-sensing acoustic transmitters across these two arrays and monitored for up to 550 days. An existing network of coastal receivers was used to examine large-scale movement patterns. Individuals exhibited varying residency, but all had small activity-space sizes within the arrays. The species utilised shallow rocky-reef habitat, displaying unimodal or bimodal patterns in depth use. A positive correlation was observed between wind speed and the detection depth of fish, with fish being likely to move to deeper water to escape periods of adverse conditions. Detection frequency data, corrected using sentinel tags, generally illustrated diurnal behaviour. Patterns of habitat usage, residency and spatial utilisation highlighted the susceptibility of G. elevata to recreational fishing pressure. The results from the present study will further contribute to the spatial information required in the zoning of effective marine protected areas, and our understanding of temperate reef fish ecology.

scholarly journals Semantic overlay network for large-scale spatial information indexing

2013 ◽  
Vol 57 ◽  
pp. 208-217 ◽  
Author(s):  
Zhiqiang Zou ◽  
Yue Wang ◽  
Kai Cao ◽  
Tianshan Qu ◽  
Zhongmin Wang
Keyword(s):  
Large Scale ◽  
Spatial Information ◽  
Overlay Network

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

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