scholarly journals Threshold based Support Vector Machine Learning Algorithm for Sequential Patterns

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
S Imavathy ◽  
M. Chinnadurai
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Pattern Mining ◽  
Research Work ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Support Vector ◽  
Positive Rate

Now a days the pattern recognition is the major challenge in the field of data mining. The researchers focus on using data mining for wide variety of applications like market basket analysis, advertisement, and medical field etc., Here the transcriptional database is used for all the conventional algorithms, which is based on daily usage of object and/or performance of patients. Here the proposed research work uses sequential pattern mining approach using classification technique of Threshold based Support Vector Machine learning (T-SVM) algorithm. The pattern mining is to give the variable according to the user’s interest by statistical model. Here this proposed research work is used to analysis the gene sequence datasets. Further, the T-SVM technique is used to classify the dataset based on sequential pattern mining approach. Especially, the threshold-based model is used for predicting the upcoming state of interest by sequential patterns. Because this makes deeper understanding about sequential input data and classify the result by providing threshold values. Therefore, the proposed method is efficient than the conventional method by getting the value of achievable classification accuracy, precision, False Positive rate, True Positive rate and it also reduces operating time. This proposed model is performed in MATLAB in the adaptation of 2018a.

scholarly journals A study on sequential pattern mining on chemical information

2018 ◽  
Vol 7 (3.3) ◽  
pp. 532
Author(s):  
S Sathya ◽  
N Rajendran
Keyword(s):  
Data Mining ◽  
Chemical Bonding ◽  
Sequential Analysis ◽  
Pattern Mining ◽  
Fundamental Problem ◽  
Research Work ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Graph Representation ◽  
Chemical Information

Data mining (DM) is used for extracting the useful and non-trivial information from the large amount of data to collect in many and diverse fields. Data mining determines explanation through clustering visualization, association and sequential analysis. Chemical compounds are well-defined structures compressed by a graph representation. Chemical bonding is the association of atoms into molecules, ions, crystals and other stable species which frame the common substances in chemical information. However, large-scale sequential data is a fundamental problem like higher classification time and bonding time in data mining with many applications. In this work, chemical structured index bonding is used for sequential pattern mining. Our research work helps to evaluate the structural patterns of chemical bonding in chemical information data sets.  

scholarly journals HIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM

2020 ◽  
Vol 36 (1) ◽  
pp. 1-15
Author(s):  
Tran Huy Duong ◽  
Nguyen Truong Thang ◽  
Vu Duc Thi ◽  
Tran The Anh
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Sequence Database ◽  
Mining Algorithm ◽  
Pattern Growth ◽  
High Utility ◽  
Growth Approach

High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm's performance.

scholarly journals DRL-Prefixspan: A novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns

2012 ◽  
Vol 2 (4) ◽  
Author(s):  
Aloysius George ◽  
D. Binu
Keyword(s):  
Data Mining ◽  
Efficient Algorithm ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Experimental Results ◽  
Sequential Pattern ◽  
Product Launch ◽  
Sequential Patterns ◽  
Sequential Mining ◽  
One Step

AbstractDiscovering sequential patterns is a rather well-studied area in data mining and has been found many diverse applications, such as basket analysis, telecommunications, etc. In this article, we propose an efficient algorithm that incorporates constraints and promotion-based marketing scenarios for the mining of valuable sequential patterns. Incorporating specific constraints into the sequential mining process has enabled the discovery of more user-centered patterns. We move one step ahead and integrate three significant marketing scenarios for mining promotion-oriented sequential patterns. The promotion-based market scenarios considered in the proposed research are 1) product Downturn, 2) product Revision and 3) product Launch (DRL). Each of these scenarios is characterized by distinct item and adjacency constraints. We have developed a novel DRL-PrefixSpan algorithm (tailored form of the PrefixSpan) for mining all length DRL patterns. The proposed algorithm has been validated on synthetic sequential databases. The experimental results demonstrate the effectiveness of incorporating the promotion-based marketing scenarios in the sequential pattern mining process.

scholarly journals HIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM

2020 ◽  
Vol 36 (1) ◽  
pp. 1-15
Author(s):  
Tran Huy Duong ◽  
Nguyen Truong Thang ◽  
Vu Duc Thi ◽  
Tran The Anh
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Sequence Database ◽  
Mining Algorithm ◽  
Pattern Growth ◽  
High Utility ◽  
Growth Approach

High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm's performance.

scholarly journals Integration of synthetic minority oversampling technique for imbalanced class

2019 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Noviyanti Santoso ◽  
Wahyu Wibowo ◽  
Hilda Hikmawati
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Class Imbalance ◽  
Original Data ◽  
Support Vector ◽  
Classification Methods ◽  
Problematic Issue ◽  
Imbalanced Class ◽  
F Measure

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.

scholarly journals Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

2020 ◽  
Author(s):  
Castro Mayleen Dorcas Bondoc ◽  
Tumibay Gilbert Malawit
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Video Tutorials ◽  
Learning Management ◽  
Face To Face Instruction

Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.

Applications of Pattern Discovery Using Sequential Data Mining

2012 ◽  
pp. 1-23 ◽  
Author(s):  
Manish Gupta ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Intrusion Detection ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Sequential Data ◽  
Mining Methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

scholarly journals Sequential Pattern Mining Algorithm Based on Text Data: Taking the Fault Text Records as an Example

2018 ◽  
Vol 10 (11) ◽  
pp. 4330 ◽  
Author(s):  
Xinglong Yuan ◽  
Wenbing Chang ◽  
Shenghan Zhou ◽  
Yang Cheng
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Fault Classification ◽  
Sequential Patterns ◽  
Series Data ◽  
Similarity Measurement ◽  
Text Similarity ◽  
Text Data

Sequential pattern mining (SPM) is an effective and important method for analyzing time series. This paper proposed a SPM algorithm to mine fault sequential patterns in text data. Because the structure of text data is poor and there are many different forms of text expression for the same concept, the traditional SPM algorithm cannot be directly applied to text data. The proposed algorithm is designed to solve this problem. First, this study measured the similarity of fault text data and classified similar faults into one class. Next, this paper proposed a new text similarity measurement model based on the word embedding distance. Compared with the classic text similarity measurement method, this model can achieve good results in short text classification. Then, on the basis of fault classification, this paper proposed the SPM algorithm with an event window, which is a time soft constraint for obtaining a certain number of sequential patterns according to needs. Finally, this study used the fault text records of a certain aircraft as experimental data for mining fault sequential patterns. Experiment showed that this algorithm can effectively mine sequential patterns in text data. The proposed algorithm can be widely applied to text time series data in many fields such as industry, business, finance and so on.

Detecting Implicit Security Exceptions Using an Improved Variable-Length Sequential Pattern Mining Method

2017 ◽  
Vol 27 (08) ◽  
pp. 1235-1268
Author(s):  
Jinfu Chen ◽  
Saihua Cai ◽  
Dave Towey ◽  
Lili Zhu ◽  
Rubing Huang ◽  
...  
Keyword(s):  
Visual Inspection ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Variable Length ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Mining Method ◽  
Security Testing ◽  
String Searching ◽  
Correct Execution

The process of component security testing can produce massive amounts of monitor logs. Current approaches to detect implicit security exceptions (those which cannot be identified by visual inspection alone) compare correct execution sequences with fixed patterns mined from the execution of sequential patterns in the monitor logs. However, this is not efficient and is not suitable for mining large monitor logs. To enable effective mining of implicit security exceptions from large monitor logs, this paper proposes a method based on improved variable-length sequential pattern mining. The proposed method first mines the variable-length sequential patterns from correct execution sequences and from actual execution sequences, thus reducing the number of patterns. The sequential patterns are then detected using the Sunday string-searching algorithm. We conducted an experimental study based on this method, the results of which show that the proposed method can efficiently detect the implicit security exceptions of components.

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