التفاصيل البيبلوغرافية
| العنوان: |
Parallel Methods for Mining Frequent Sequential patterns |
| المؤلفون: |
Huynh, Quoc Bao |
| المساهمون: |
Snášel, Václav, Platoš, Jan, Zendulka, Jaroslav, Šenkeřík, Roman |
| بيانات النشر: |
Vysoká škola báňská - Technická univerzita Ostrava |
| سنة النشر: |
2017 |
| المجموعة: |
DSpace VŠB-TUO (Vysoká škola báňská - Technická univerzita Ostrava / Technical University of Ostrava) |
| مصطلحات موضوعية: |
Data Mining, Frequent Pattern, Sequential Pattern, Parallel Mining, Dynamic Load Balancing, Multi-core processors, Sequence Database, Large Sequence Database |
| الوصف: |
The explosive growth of data and the rapid progress of technology have led to a huge amount of data that is collected every day. In that data volume contains much valuable information. Data mining is the emerging field of applying statistical and artificial intelligence techniques to the problem of finding novel, useful and non-trivial patterns from large databases. It is the task of discovering interesting patterns from large amounts of data. This is achieved by determining both implicit and explicit unidentified patterns in data that can direct the process of decision making. There are many data mining tasks, such as classification, clustering, association rule mining and sequential pattern mining. In that, sequential pattern mining is an important problem in data mining. It provides an effective way to analyze the sequence data. The goal of sequential pattern mining is to discover interesting, unexpected and useful patterns from sequence databases. This task is used in many wide applications such as financial data analysis of banks, retail industry, customer shopping history, goods transportation, consumption and services, telecommunication industry, biological data analysis, scientific applications, network intrusion detection, scientific research, etc. Different types of sequential pattern mining can be performed, they are sequential patterns, maximal sequential patterns, closed sequences, constraint based and time interval based sequential patterns. Sequential pattern mining refers to the identification of frequent subsequences in sequence databases as patterns. In the last two decades, researchers have proposed many techniques and algorithms for extracting the frequent sequential patterns, in which the downward closure property plays a fundamental role. Sequential pattern is a sequence of itemsets that frequently occur in a specific order, where all items in the same itemsets are supposed to have the same transaction time value. One of the challenges for sequential pattern mining is the computational ... |
| نوع الوثيقة: |
thesis |
| وصف الملف: |
108 listů : ilustrace; 3806704 bytes; application/pdf |
| اللغة: |
English |
| العلاقة: |
OSD002; http://hdl.handle.net/10084/127363Test; 201800026; ÚK/Studovna; S2724; HUY0009_FEI_P1807_1801V001_2017 |
| الإتاحة: |
http://hdl.handle.net/10084/127363Test |
| حقوق: |
openAccess |
| رقم الانضمام: |
edsbas.D9F83571 |
| قاعدة البيانات: |
BASE |
