Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted ...

With the widespread of location-based social networks (LBSNs), the amount of check-in data grows rapidly, which helps to recommend the next point-of-interest (POI). Extracting sequential patterns from check-in data has become a meaningful way for next POI ...

Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers ...

Mining High Utility Sequential Patterns (HUSP) is an emerging topic in data mining which attracts many researchers. The HUSP mining algorithms can extract sequential patterns having high utility (importance) in a quantitative sequence database. ...

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and ...

Trajectory pattern mining has been performed over many datasets, including animal movement, GPS trajectories, and human travel history. This paper aims to explore and mine individual frequently visited regions and trajectory patterns using online ...

Sequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship ...

Recently, due to the great advent of sensor technology, residents can collect household appliance usage data easily. However, in general, usage data are generated progressively, visualizing how appliances are used from huge amount of data is ...

We present a new methodology for sequential classification, which employs sequential pattern generation and classification, in a two-stage process. In the first phase, a set of sequential patterns are generated from multi-dimensional sequence data. We ...

Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much ...

To survive in a rapidly changing environment, theme parks need to provide high quality services in terms of visitor tastes and preferences. Understanding the spatial and temporal behavior of visitors could enhance the attraction management and ...

Recognising high-level human activities from low-level sensor data is a crucial driver for pervasive systems that wish to provide seamless and distraction-free support for users engaged in normal activities. Research in this area has grown alongside ...

Original sequential pattern mining model only considers occurrence frequentness of sequential patterns, disregards their occurrence periodicity. We propose the asynchronous periodic sequential pattern mining model to discover the sequential patterns ...

Within the last few years the importance of collaborative ontology-engineering projects, especially in the biomedical domain, has drastically increased. This recent trend is a direct consequence of the growing complexity of these structured data ...

With the rapid development of online learning technology, a huge amount of e-learning materials have been generated which are highly heterogeneous and in various media formats. Besides, e-learning environments are highly dynamic with the ever increasing ...

Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much ...

As an important part of Web application security, a web access log records the attack traces of malicious users. But it's hard to accurately capture the attack behaviors from massive amounts of logs. A security analysis of web users' behavior based on ...

Sequential generator pattern mining is an important task in data mining. Sequential generator patterns used together with closed sequential patterns can provide additional information that closed sequential patterns alone are not able to provide. In ...

Many XML document clustering algorithms need to compute similarity among documents. Due to its semi-structured characteristic, exploiting the structure information for computing structural similarity is a crucial issue in XML similarity computation. ...

In this paper, we describe a Web log mining approach to recommend learning items for each active learner based on the learner's historical learning track. The proposed method is composed of three parts: discovering content-related item sets by ...