scholarly journals Mining Fuzzy Time Interval Periodic Patterns in Smart Home Data

2018 ◽  
Vol 8 (5) ◽  
pp. 3374
Author(s):  
Imam Mukhlash ◽  
Desna Yuanda ◽  
Mohammad Iqbal
Keyword(s):  
Data Mining ◽  
Smart Home ◽  
Pattern Mining ◽  
Technology Development ◽  
Sensor Data ◽  
Mining Machine ◽  
Periodic Pattern ◽  
Time Interval ◽  
Periodic Patterns ◽  
Using Data

A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident’s habits.

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

The Application of Data Mining Technology in Mechanical Fault Diagnosis

2011 ◽  
Vol 460-461 ◽  
pp. 821-826
Author(s):  
Yun Feng Lin ◽  
Xiao Ping Hu
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Technology Development ◽  
Development Trend ◽  
Advanced Technology ◽  
Mining Technology ◽  
Intelligent Diagnosis ◽  
Hidden Knowledge ◽  
Using Data ◽  
Development Tendency

This article first introduced the survey of mechanical fault diagnosis technology development and the data mining technology theory. Then its application situation at present and the main questions that exist are elaborated. Its development trend is analyzed. The application feasibility of using data mining technology in mechanical fault diagnosis is discussed. Next, the naissance, the development and the future development tendency of data mining technology are introduced. The four algorithms are analyzed and the framework is built too. Intelligent Diagnosis is a major development direction of the fault diagnosis. Knowledge acquisition is the bottleneck of Intelligent Diagnosis development. It comprehensive use of many kinds of advanced technology, discover valuable and hidden knowledge from the large amounts of data mining.

scholarly journals Analysisof Students’ Web Browsing Behaviours Using Data Miningat a Campus Network

2021 ◽  
Vol 12 (6) ◽  
pp. 2726-2738
Author(s):  
Murizah Kassim, Muhammad Ahlami Ashraf Roslan
Keyword(s):  
Data Mining ◽  
Monitoring System ◽  
Mining Machine ◽  
Campus Network ◽  
Time Data ◽  
Network Computer ◽  
Indexing Method ◽  
R Programming ◽  
Using Data ◽  
Browsing Behavior

Analytics provides insight to people based on the analytics of past usage by using techniques such as statistics, data mining, machine learning and artificial intelligence. Lack of monitoring system of browsing causes low engagements that reduce the growth of certain businesses caused by unnecessary browsing for students learning time. This paper presents an analysis on browsing behavior that classifies browsed words followed their ethical word-groups browsing. An Analytic platform is created as a monitoring system of browsing behavior. Data mining, indexing and classification method are used in this research as data is the essential key of creating a predictive model and four types of ethical groups have been filtered based on the browsing behaviors. The browsed words are categorized into four types of browsing called queries, applications, social media, Campus-related sites. The research method uses software tools and data mining process on the browsing data and analytics is presented on the development of the dashboard mainly using the R programming language. Few unethical words using the indexing method are generated in analytic graphs based on the type of browsing versus time. Data collected from the browsing behaviors of students’analysis taken from browsing database of personal computer and laboratory computer in the campus network. The result shows that othercategories are the highest categories which reached79.6% for personals' computer browsing compared to72.4% browsing at the laboratory computers. It is identified that about 21% of the browsing behavior was filtered during the data mined processed. The other category is still on the research portfolio where these libraries must be filtered in detail to identify whether they are learning or non-learning activities. This research is significant in that helps to increase the effectiveness of suggestions applications, optimize the internet usage by blocking unnecessary words or webpages, and even campus guide systems by monitoring the surrounding browsing behavior of the students’ usages of the campus network computer labs.

scholarly journals Using Data Mining In Learning Management Systems Amidst Covid-19

2020 ◽  
Vol 6 (3) ◽  
pp. 213
Author(s):  
Froilan D Mobo
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Learning Management Systems ◽  
The Philippines ◽  
Management Systems ◽  
Official Gazette ◽  
Learning Management ◽  
The Republic ◽  
Using Data ◽  
The One

<p>The Second Semester of Academic Year 2019-2020 was temporarily suspended due to the widespread COVID-19 last March 16, 2020, forcing the President of the Republic of the Philippines, Hon. Rodrigo Roa Duterte imposed an Enhanced Community Quarantine in Luzon which is known as a lockdown closing all the border points of each town and provinces. One of the major problem encountered during the lockdown is the suspension of classes because as per IATF guidelines you need to stay home, the said Memorandum Order was posted in the official gazette, (Medialdea, 2020)</p><p>The dataset on the features of the Learning Management Systems using Moodle is that Professors will be the one who will set the topics, quizzes, and exercises for his class even the assessment methods on the system. To prevent from slowing down the network,  the Team of Seaversity the developer of the learning management systems headed by C/E Ephrem Dela Cernan conducts a ZOOM Training to all Faculty to be familiarized more on the Learning Management Systems of the Philippine Merchant Marine Academy. </p><p>The Moodle Learning Management Systems is a user-friendly environment because of its features and users can easily adjust from the traditional face to face teaching going to e-Learning approach because of it’s all capabilities as a data mining methods such as statistics, association rule mining, pattern mining visualization, categorization, clustering, and text mining., (AlAjmi &amp; Shakir, 2013)</p>

scholarly journals Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

2017 ◽  
Author(s):  
Wala Ismail ◽  
Mohammad Mehedi Hassan
Keyword(s):  
Vital Sign ◽  
Smart Home ◽  
Healthcare Providers ◽  
The Elderly ◽  
Frequent Pattern ◽  
Sensor Data ◽  
Body Sensor Networks ◽  
Periodic Patterns ◽  
Frequent Items

The understanding of various health-oriented vital sign data generated from body sensor networks (BSN) and discovery of the association between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where the occupants’ health status is continuously monitored remotely, it is essential to provide required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the incomplete (partial) periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce the productive-associated partial periodic frequent patterns as the set of correlated partial periodical frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients for quality of diagnosis, and also for better treatment and smart care, especially for the elderly people at smart home. We developed an efficient algorithm named PPFP-Growth (Productive Periodic Frequent Pattern growth) to discover all productive associated partial periodic patterns using these measures. PPFP-Growth is efficient, and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-Growth algorithm, and can filter a huge number of partial periodic patterns to reveal only the correlated ones.

Optimal Ordering Policy With Inventory Classification Using Data Mining Techniques

2018 ◽  
pp. 305-326 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Optimal Order ◽  
Order Quantity ◽  
Data Mining Techniques ◽  
Ordering Policy ◽  
Frequent Items ◽  
Optimal Ordering Policy ◽  
Using Data ◽  
Research Studies

Data mining is a technique to identify valid novel, potentially useful, and understandable correlations and patterns in existing data. Data mining techniques, such as clustering, association rule mining, classification, and sequential pattern mining, have attracted a great deal of attention in the information industry and in society as a whole in recent years. Some research studies have also extended the usage of this concept in inventory management. Yet, not many research studies have considered the application of data mining approach on determining both optimal order quantity and loss profit of frequent items. This helps inventory manager to determine optimum order quantity of frequent items together with the most profitable item for optimal inventory control. In this chapter, two different cases for determining ordering policy and inventory classification based on loss rule are presented. An example is illustrated to validate the results.

Mining Dense Periodic Patterns in Time Series Databases

2008 ◽  
pp. 44-62
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Real Life ◽  
Search Space ◽  
Detection Algorithm ◽  
Limited Range ◽  
Periodic Pattern ◽  
Periodic Patterns ◽  
Pruning Strategy ◽  
Synthetic Datasets

In this chapter, we describe a new periodicity detection algorithm to efficiently discover short period patterns that may exist in only a limited range of the time series. We refer to these patterns as the dense periodic patterns, where the periodicity is focused on part of the time series. We present a dense periodic pattern mining algorithm called DPMiner to find dense periodic patterns, and design a pruning strategy to limit the search space to the feasible periods. Experimental results on both real-life and synthetic datasets indicate that DPMiner is both scalable and efficient.

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