A text representation model using Sequential Pattern-Growth method

Alias, Suraya; Mohammad, Siti Khaotijah; Gan, Keng Hoon; Ping, Tan Tien

doi:10.1007/s10044-017-0624-9

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…Poor results in terms of the precision or relevance of the information that produceed does not mean that the WIDF and VSM algorithms are wrong, However, it is because the text representation that used (bagof word or 1-gram) is not good in maintaining the meaning of text documents. Nowadays, there are many Text Mining researches that prove and use multiple of words or n-grams that can maintain the meaning of text better [56][57][58][59][60][61]. Even Google search engines that implement IR and TM technology do not use 1-gram, because when we search for the word "Information Retrieval" (without quoting as input keywords) on Google search engines, the information that will be generated is related to "Information", "Retrieval" and "Information Retrieval".…”

Section: Analysis and Evaluation Of Experiments And Testing Resultsmentioning

confidence: 99%

Weighted inverse document frequency and vector space model for hadith search engine

Pratama

Darmalaksana

Maylawati

et al. 2020

IJEECS

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Hadith is the second source of Islamic law after Qur’an which make many types and references of hadith need to be studied. However, there are not many Muslims know about it and many even have difficulties in studying hadiths. This study aims to build a hadith search engine from reliable source by utilizing Information Retrieval techniques. The structured representation of the text that used is Bag of Word (1-term) with the Weighted Inverse Document Frequency (WIDF) method to calculate the frequency of occurrence of each term before being converted in vector form with the Vector Space Model (VSM). Based on the experiment results using 380 texts of hadith, the recall value of WIDF and VSM is 96%, while precision value is just around 35.46%. This is because the structured representation for text that used is bag of words (1-gram) that can not maintain the meaning of text well).

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Section: Analysis and Evaluation Of Experiments And Testing Resultsmentioning

confidence: 99%

Weighted inverse document frequency and vector space model for hadith search engine

Pratama

Darmalaksana

Maylawati

et al. 2020

IJEECS

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“…For example, given that HUCSPs maintain the contiguous order of items in sequence data, it can be applied for next-items recommendation [28]. For some practical issues, such as text representation [48] and biological sequence discovery [49], where the adjacent relationship of items is significant, UCSPM can also come in handy.…”

Section: Discussionmentioning

confidence: 99%

Utility-driven Mining of Contiguous Sequences

Zhang¹,

Dai²,

Du³

et al. 2021

Preprint

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Recently, contiguous sequential pattern mining (CSPM) gained interest as a research topic, due to its varied potential real-world applications, such as web log and biological sequence analysis. To date, studies on the CSPM problem remain in preliminary stages. Existing CSPM algorithms lack the efficiency to satisfy users' needs and can still be improved in terms of runtime and memory consumption. In addition, existing algorithms were developed to deal with simple sequence data, working with only one event at a time. Complex sequence data, which represent multiple events occurring simultaneously, are also commonly observed in real life. In this paper, we propose a novel algorithm, fast utility-driven contiguous sequential pattern mining (FUCPM), to address the CSPM problem. FUCPM adopts a compact sequence information list and instance chain structures to store the necessary information of the database and candidate patterns. For further efficiency, we develop the global unpromising items pruning and local unpromising items pruning strategies, based on sequence-weighted utilization and item-extension utilization, to reduce the search space. Extensive experiments on real-world and synthetic datasets demonstrate that FUCPM outperforms the state-of-the-art algorithms and is scalable enough to handle complex sequence data.

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“…Text mining is the new frontier of content analysis [88] and offers advanced techniques for intelligent data analytics, in which algorithms and statistical models make it possible to extract previously unknown and potentially useful knowledge from large-scale, text-based databases and repositories [89,90]. Text mining covers a broad range of techniques [91], and some of the most commonly used include clustering [88], association rule discovery [92], sequential patterns [93,94], anomaly detection [95][96][97], regression [98,99], and segmentation [100,101]. The selection of a technique depends upon the reason for conducting the text mining analysis, which can be either exploratory or predictive.…”

Section: Content Analysis With Text Miningmentioning

confidence: 99%

Closing the Affordable Housing Gap: Identifying the Barriers Hindering the Sustainable Design and Construction of Affordable Homes

Reid

2023

Sustainability

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Despite the commitment of the United Nations (UN) to provide everyone with equal access to basic services, the construction sector still fails to reach the production capacity and quality standards which are needed to meet the fast-growing demand for affordable homes. Whilst innovation measures are urgently needed to address the existing inefficiencies, the identification and development of the most appropriate solutions require a comprehensive understanding of the barriers obstructing the design and construction phase of affordable housing. To identify such barriers, an exploratory data mining analysis was conducted in which agglomerative hierarchical clustering made it possible to gather latent knowledge from 3566 text-based research outputs sourced from the Web of Science and Scopus. The analysis captured 83 supply-side barriers which impact the efficiency of the value chain for affordable housing provision. Of these barriers, 18 affected the design and construction phase, and after grouping them by thematic area, seven key matters of concern were identified: (1) design (not) for all, (2) homogeneity of provision, (3) unhealthy living environment, (4) inadequate construction project management, (5) environmental unsustainability, (6) placemaking, and (7) inadequate technical knowledge and skillsets. The insights which resulted from the analysis were seen to support evidence-informed decision making across the affordable housing sector. The findings suggest that fixing the inefficiencies of the affordable housing provision system will require UN Member States to accelerate the transition towards a fully sustainable design and construction process. This transition should prioritize a more inclusive and socially sensitive approach to the design and construction of affordable homes, capitalizing on the benefits of greater user involvement. In addition, transformative actions which seek to deliver more resource-efficient and environmentally friendly homes should be promoted, as well as new investments in the training and upskilling of construction professionals.

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A text representation model using Sequential Pattern-Growth method

Cited by 10 publications

References 29 publications

Weighted inverse document frequency and vector space model for hadith search engine

Weighted inverse document frequency and vector space model for hadith search engine

Utility-driven Mining of Contiguous Sequences

Closing the Affordable Housing Gap: Identifying the Barriers Hindering the Sustainable Design and Construction of Affordable Homes

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