Parallel Methods for Mining Frequent Sequential patterns

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 costs beside that is the potentially huge number of extracted patterns. In this thesis, we present an overview of the work done for sequential pattern mining and develop parallel methods for mining frequent sequential patterns in sequence databases that can tackle emerging data processing workloads while coping with larger and larger scales.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 costs beside that is the potentially huge number of extracted patterns. In this thesis, we present an overview of the work done for sequential pattern mining and develop parallel methods for mining frequent sequential patterns in sequence databases that can tackle emerging data processing workloads while coping with larger and larger scales.460 - Katedra informatikyvyhově

Mining Target-Oriented Sequential Patterns with Time-Intervals

A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets. In this paper we present an algorithm to discover target-oriented sequential pattern with time-intervals. To this end, the original sequences are reversed so that the last itemsets can be arranged in front of the sequences. The contrasts between reversed sequences and the concerned itemset are then used to exclude the irrelevant sequences. Clustering analysis is used with typical sequential pattern mining algorithm to extract the sequential patterns with time-intervals between successive itemsets. Finally, the discovered time-interval sequential patterns are reversed again to the original order for searching the target patterns.Comment: 11 pages, 9 table

A qualitative approach to the identification, visualisation and interpretation of repetitive motion patterns in groups of moving point objects

Discovering repetitive patterns is important in a wide range of research areas, such as bioinformatics and human movement analysis. This study puts forward a new methodology to identify, visualise and interpret repetitive motion patterns in groups of Moving Point Objects (MPOs). The methodology consists of three steps. First, motion patterns are qualitatively described using the Qualitative Trajectory Calculus (QTC). Second, a similarity analysis is conducted to compare motion patterns and identify repetitive patterns. Third, repetitive motion patterns are represented and interpreted in a continuous triangular model. As an illustration of the usefulness of combining these hitherto separated methods, a specific movement case is examined: Samba dance, a rhythmical dance will? many repetitive movements. The results show that the presented methodology is able to successfully identify, visualize and interpret the contained repetitive motions