Irrelevant Feature and Rule Removal for Structural Associative Classification

Shaharanee, Izwan Nizal Mohd; Jamil, Jastini Mohd

doi:10.32890/jict2015.14.0.8158

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…However, the focus of this paper is on the segmentation technique which uses edge-based method and mathematical morphology. Segmentation process is crucial because the quality of segmented image affects the results of feature extraction and classification task (Shaharanee & Jastini, 2015). The extraction task transforms sufficient content of images into various content features or attributes.…”

Section: Methodsmentioning

confidence: 99%

The Fusion of Edge Detection and Mathematical Morphology Algorithm for Shape Boundary Recognition

Othman

Abdullah

Ahmad

et al. 2016

JICT

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Edge detection is important in image analysis to form the shape of an object. Edge is the boundary between different textures, which helps with object segmentation and recognition. Currently, several edge detection techniques are able to identify objects but are unable to localize the shape of an object. To address this problem, this paper proposes a fusion of selected edge detection algorithms with mathematical morphology to enhance the ability to detect the object shape boundary. Edge detection algorithm is used to simplify image data by minimizing the amount of pixel to be processed, whereas the mathematical morphology is used for smoothing effects and localizing the object shape using mathematical theory sets. The discussion section focuses on the improved edge map and boundary morphology (EmaBm) algorithm as a new technique for shape boundary recognition. A comparative analysis of various edge detection algorithms is presented. It reveals that the LoG's edge detection embedded in EmaBM algorithm performs better than the other edge detection algorithms for fruit shape boundary recognition. Implementation of the proposed method shows that it is robust and applicable for various kind of fruit images and is more accurate than the existing edge detection algorithms.

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Section: Methodsmentioning

confidence: 99%

The Fusion of Edge Detection and Mathematical Morphology Algorithm for Shape Boundary Recognition

Othman

Abdullah

Ahmad

et al. 2016

JICT

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“…Nevertheless, the performance of FSM will drop when the rules are produced neither interesting nor useful. Shaharanee and Jamil [32] propose that removing variables that are not relevant can reduce the rules that are not interesting. Moreover, the XML documents' structure properties usually neglected by FSM.…”

Section: Introductionmentioning

confidence: 99%

Framework to Mine XML Format Event Logs

Ang¹,

Jamil²,

Shaharanee³

2022

ijasca

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A lot of applications including event logs and web pages uses XML format for utilizing, keeping, transferring and displaying data. Thus, volume of data expressed in XML has increase rapidly. Numerous research has been done to extract and mine information from XML documents. Mining XML documents allows an understanding to the architecture and composition of XML documents. Generally, frequent subtree mining is one of the methods to mine XML documents. Frequent subtree mining searches the relation between data in a tree structured database. Due to the architecture and the composition of XML format, normal data mining and statistical analysis difficult to be performed. This paper suggests a framework that flattens and converts tree structured data into structured data, while maintaining the information of architecture and the composition of XML format. To gain more information from event logs, converting into structured data from semistructured format grants more ability to perform variety data mining techniques and statistical test. Keywords: Flatten Sequential Structure Model, XML Format Event Logs, Data Mining, Statistical Analysis.

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“…However, FSM performance will be decrease when the rules generated are not interesting or useful. Shaharanee and Jamil (2015) suggest that filtering irrelevant variables can prevent the case of generating not interesting rules. Furthermore, the structural properties of XML documents usually ignored by FSM.In short, framework that account structural properties of XML and able to apply statistical analysis in XML data is needed to extract more information from business process logs.…”

Section: Introductionmentioning

confidence: 99%

A Framework to Analyze Business Process Log in XML Format

et.al¹

2021

TURCOMAT

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XML has numerous uses in a wide variety of web pages and applications. Some common uses of XML include tasks for web publishing, web searching and automation, and general application such as for utilize, store, transfer and display business process log data. The amount of information expressed in XML has gone up rapidly. Many works have been done on sensible approaches to address issues related to the handling and review of XML documents. Mining XML documents offera way to understand both the structure and the content of XML documents. A common approach capable of analysing XML documents is frequent subtree mining.Frequent subtree mining is one of the data mining techniques that finds the relationship between transactions in a tree structured database. Due to the structure and the content of XML format, traditional data mining and statistical analysis hardly applied to get accurate result. This paper proposes a framework that can flatten a tree structured data into a flat and structured data, while preserving their structure and content.Enabling these XML documents into relational structured data allows a range of data mining techniques and statistical test can be applied and conducted to extract more information from the business process log.

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Irrelevant Feature and Rule Removal for Structural Associative Classification

Cited by 6 publications

References 22 publications

The Fusion of Edge Detection and Mathematical Morphology Algorithm for Shape Boundary Recognition

The Fusion of Edge Detection and Mathematical Morphology Algorithm for Shape Boundary Recognition

Framework to Mine XML Format Event Logs

A Framework to Analyze Business Process Log in XML Format

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