Categorical Data Skyline Using Classification Tree

Lee, Wookey; Song, Jung‐Hun; Leung, Carson K.

doi:10.1007/978-3-642-20291-9_19

Cited by 16 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the possible alternatives for the social network search, computation and structuring of every arc usually focus on expressing the whole social network graph [1,3,7,22]. Nevertheless, from the perspective of navigation, redundant hyperlinks can lead to excessive cost to operate computing each and every links [5,12,24], and overly complicated process of considering the circuit path problem which causes inefficiency of the social network search [7,10,11,13,21]. Hence, unlike typical network analysis, it is enormously significant in social network search to generate an efficient graph and for its structure search.…”

Section: Methodsmentioning

confidence: 99%

“…Hence, unlike typical network analysis, it is enormously significant in social network search to generate an efficient graph and for its structure search. Retranslating social network graphs into tree forms for social network search, and searching for information within the tree have been known more effective [1,6,13]. Searching for the tree not only reduces the searching time, but also creates advantage of enabling quick search adopting additional data structure (e.g., index or metadata) [2,12,20,27].…”

Section: Methodsmentioning

confidence: 99%

“…We validate the superiority of our method by comparing with the OBT (Optimum Branching Tree) that is the most used among many methods for making graph to tree. Recently studies which can indicate the plausibility of showing web pages through cost computation between links using certain measures, and translating web pages into web graphs that are being extensively conducted [7,[11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Maximum Reliable Tree for Social Network Search

Lee

Song

et al. 2011

2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing

Self Cite

View full text Add to dashboard Cite

As the overall size of the social networks has been exponentially expanding; the technological advance of efficient social networks search can flourish in the academic sphere, business corporations, and public institutions. Additionally, facing environmental upheaval in the field of information fusion and consilience, an effective development of various social network searches is gaining significance. In this research, we propose the use of the Maximum Reliable Tree algorithm that is newly developed based on a graph-based method, as a generic technique which facilitates effective social network search and that can be the most reliable social network search method for the promptly appearing smart phone technologies. We presented the method's excellence in performance by demonstrating core arguments in formal descriptions, and illustrating experimental results.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximum Reliable Tree for Social Network Search

Lee

Song

et al. 2011

2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Skyline queries are more often used for filtering numerical data, as in research [3], [4], and [5]. Unlike other studies that use numerical data, research [7] uses categorical data, namely the Association of Computing Machinery Computing Classification System (ACM CCS) ontology and the ACM CCS literature. Each node in the ontology is a keyword that represents literature.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Functions in Categorical Data Skyline Search (CDSS) for Multi-keyword Document Search

Mardiah¹,

Annisa²,

Neyman³

2023

khif

View full text Add to dashboard Cite

Literature review is the first step in starting research for a deep understanding of the research interest. However, finding literature relevant to research interests is difficult and takes time. Skyline query is a method that can be used for filtering. An object p is said to dominate object q if p equals q on all of its attributes, and p is at least better than q on one attribute. Categorical Data Skyline Search (CDSS) is an algorithm that can filter skyline objects in categorical data types such as documents. CDSS uses Extended Distance Wu and Palmer (DEWP) to calculate the distance between the user query and document keywords. The document keywords and user queries are represented as nodes in the ACM CCS ontology, and documents are assumed to be represented by a single keyword. This study aims to use the CDSS algorithm to search for skyline documents represented by more than one keyword by adding an aggregate function (average, minimum, maximum) to the CDSS algorithm, especially in calculating DEWP. This study used the thesis documents from the IPB University computer science department. Document keywords will be extracted using the Term Frequency-Inverse Term Frequency (TF-IDF) method. The collected keywords will be mapped in a mixed ontology tree that refers to the Association of Computing Machinery Computing Classification System 2012 (ACM CCS 2012) and Computer Science Ontology (CSO) as ontology standards in computer science. The skyline query algorithm for determining skyline documents is Block Nested Loop (BNL). The evaluation method uses the skyline ratio of each aggregate function in the CDSS. Based on the ratio value, CDSS using the maximum DEWP has the most relevant skyline results compared to the average DEWP and minimum DEWP.

show abstract

“…Common data mining tasks include classification [11], social network mining [10,23,25], and frequent itemset mining [2]. The latter finds those frequently co-occurring items, objects, or events.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Probabilistic Frequent Pattern Mining by PUH-Mine

Tong

Leung

Liu

et al. 2015

Web Technologies and Applications

View full text Add to dashboard Cite

Abstract. To mine frequent itemsets from uncertain data, many existing algorithms rely on expected support based mining. An alternative approach relies on probabilistic based mining, which captures the frequentness probability. While the possible world semantics are widely used, the exponential growth of possible worlds makes the probabilistic based mining computationally challenging when compared to the expected support based mining. In this paper, we propose two efficient approximate hyperlinked structure based algorithms, which generate a collection of all potentially probabilistic frequent itemsets with a novel upper bound and verify if they are truly probabilistic frequent. Experimental results show the efficiency of our algorithms in mining probabilistic frequent itemsets from uncertain data.

show abstract

Categorical Data Skyline Using Classification Tree

Cited by 16 publications

References 14 publications

Maximum Reliable Tree for Social Network Search

Maximum Reliable Tree for Social Network Search

Aggregate Functions in Categorical Data Skyline Search (CDSS) for Multi-keyword Document Search

Probabilistic Frequent Pattern Mining by PUH-Mine

Contact Info

Product

Resources

About