A Method to Avoid Gapped Sequential Patterns in Biological Sequences: Case Study: HIV and Cancer Sequences

2017 ◽  
Vol 4 (1) ◽  
pp. 49-53
Author(s):  
Hamid Alinejad-Rokny
Keyword(s):  
Sequential Patterns ◽  
Biological Sequences ◽  
Hiv And Cancer

scholarly journals BASiNET—BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification

2018 ◽  
Vol 46 (16) ◽  
pp. e96-e96 ◽  
Author(s):  
Eric Augusto Ito ◽  
Isaque Katahira ◽  
Fábio Fernandes da Rocha Vicente ◽  
Luiz Filipe Protasio Pereira ◽  
Fabrício Martins Lopes
Keyword(s):  
Biological Sequences ◽  
Non Coding Rnas

Trend Analysis of Product Function Using Sequential Pattern Mining

2014 ◽  
Vol 519-520 ◽  
pp. 736-740
Author(s):  
Li Yu ◽  
Zai Fang Zhang
Keyword(s):  
Pattern Mining ◽  
Early Stage ◽  
Dynamic Change ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Design Engineers ◽  
Product Function ◽  
Historical Database

During the early stage of product design, it is important for design engineers to decide the most appropriate functions for various customers. To facilitate this time consuming task, sequential pattern mining is applied to uncover the useful patterns in historical database. The mined sequential patterns can reflect the dynamic change of product functions, which can help design engineers find the most suitable product functions for customers. Based on the historical sales transactions of computer, a case study is conducted to illustrate the proposed method.

Application of data mining for spare parts information in maintenance schedule: a case study

2019 ◽  
Vol 30 (7) ◽  
pp. 1055-1072
Author(s):  
U.C. Moharana ◽  
S.P. Sarmah ◽  
Pradeep Kumar Rathore
Keyword(s):  
Data Mining ◽  
Industrial Applications ◽  
Spare Parts ◽  
Sequential Patterns ◽  
Historical Records ◽  
Threshold Values ◽  
Content Type ◽  
Maintenance Activity ◽  
Maintenance Activities

Purpose The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values. Design/methodology/approach A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts. Findings This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity. Research limitations/implications Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study. Practical implications The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities. Originality/value This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity.

scholarly journals Genetic Similarity Analysis Based on Positive and Negative Sequence Patterns of DNA

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2090
Author(s):  
Yue Lu ◽  
Long Zhao ◽  
Zhao Li ◽  
Xiangjun Dong
Keyword(s):  
Dna Sequences ◽  
Sequence Similarity ◽  
Sequential Patterns ◽  
Similarity Analysis ◽  
Frequent Patterns ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Genetic Characteristics ◽  
Missing Gene ◽  
Sequence Similarity Analysis

Similarity analysis of DNA sequences can clarify the homology between sequences and predict the structure of, and relationship between, them. At the same time, the frequent patterns of biological sequences explain not only the genetic characteristics of the organism, but they also serve as relevant markers for certain events of biological sequences. However, most of the aforementioned biological sequence similarity analysis methods are targeted at the entire sequential pattern, which ignores the missing gene fragment that may induce potential disease. The similarity analysis of such sequences containing a missing gene item is a blank. Consequently, some sequences with missing bases are ignored or not effectively analyzed. Thus, this paper presents a new method for DNA sequence similarity analysis. Using this method, we first mined not only positive sequential patterns, but also sequential patterns that were missing some of the base terms (collectively referred to as negative sequential patterns). Subsequently, we used these frequent patterns for similarity analysis on a two-dimensional plane. Several experiments were conducted in order to verify the effectiveness of this algorithm. The experimental results demonstrated that the algorithm can obtain various results through the selection of frequent sequential patterns and that accuracy and time efficiency was improved.

Travel Diaries Analysis by Sequential Rule Mining

2017 ◽  
Vol 57 (3) ◽  
pp. 399-413 ◽  
Author(s):  
Huy Quan Vu ◽  
Gang Li ◽  
Rob Law ◽  
Yanchun Zhang
Keyword(s):  
Sequential Patterns ◽  
Destination Marketing ◽  
Rule Mining ◽  
Data Set ◽  
Travel Information ◽  
Sequential Rule ◽  
Travel Diaries ◽  
Geotagged Photos

Because of the inefficiency in analyzing the comprehensive travel data, tourism managers are facing the challenge of gaining insights into travelers’ behavior and preferences. In most cases, existing techniques are incapable of capturing the sequential patterns hidden in travel data. To address these issues, this article proposes to analyze the travelers’ behavior through geotagged photos and sequential rule mining. Travel diaries, constructed from the photo sequences, can capture comprehensive travel information, and then sequential patterns can be discovered to infer the potential destinations. The effectiveness of the proposed framework is demonstrated in a case study of Australian outbound tourism, using a data set of more than 890,000 photos from 3,623 travelers. The introduced framework has the potential to benefit tourism researchers and practitioners from capturing and understanding the behaviors and preferences of travelers. The findings can support destination-marketing organizations (DMOs) in promoting appropriate destinations to prospective travelers.

The Genesis and Internal Dynamics of El Salvador's People's Revolutionary Army, 1970–1976

2014 ◽  
Vol 38 (01) ◽  
pp. 102-129
Author(s):  
ALBERTO MARTÍN ÁLVAREZ ◽  
EUDALD CORTINA ORERO
Keyword(s):  
Latin America ◽  
El Salvador ◽  
Early Years ◽  
Left Wing ◽  
Internal Dynamics ◽  
Internal Coherence ◽  
The 1960S

AbstractUsing interviews with former militants and previously unpublished documents, this article traces the genesis and internal dynamics of the Ejército Revolucionario del Pueblo (People's Revolutionary Army, ERP) in El Salvador during the early years of its existence (1970–6). This period was marked by the inability of the ERP to maintain internal coherence or any consensus on revolutionary strategy, which led to a series of splits and internal fights over control of the organisation. The evidence marshalled in this case study sheds new light on the origins of the armed Salvadorean Left and thus contributes to a wider understanding of the processes of formation and internal dynamics of armed left-wing groups that emerged from the 1960s onwards in Latin America.

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