Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow

Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow. It proposes a new approach by considering the distribution of the flows in a given time interval. The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information. The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases. Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection. To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations. The results reveal that the proposed framework is able to detect the real distribution of flow outliers. Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow

Issues and Techniques of Spatio -Temporal Rule Mining for Location Based Services

The Convergence of location-aware devices, wireless communication, such as the increasing accuracy of GPS technology and geographic information system functionalities enables the deployment of new services such as location-based services (LBS). Achieve high quality or such services, spatio2013;temporal data mining techniques are needed. Our work concentrates on the development of data mining techniques for knowledge discovery and delivery in LBS. First, a number of real world spatio2013;temporal data sets are described, leading to a taxonomy of spatio2013;temporal data. Second, the paper describes a general methodology that transforms the spatio2013;temporal rule mining task to the traditional market basket analysis task and applies it to the described data sets, enabling traditional association rule mining methods to discover spatio2013;temporal rules for LBS. Finally, unique issues in spatio2013;temporal rule mining are identified and discussed

A survey of temporal knowledge discovery paradigms and methods

With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Penggunaan Algoritma T-Apriori* Untuk Pencarian Association Rule Pada Data Spatio-Temporal

Seiring dengan berkembang pesatnya aplikasi basisdata, obyek data mining juga berkembang untuk menangani tipe data yang kompleks, antara lain data spatio-temporal. Data spatio-temporal menyimpan obyek spasial dan perubahannya, baik perubahan data spasial maupun data atributnya. Pada makalah ini akan dibahas pengembangan algoritma association rule pada data spasial dengan menambahkan batasan waktu. Spatio-temporal association rule terjadi jika terdapat relasi spatio-temporal pada bagian antecedent atau consequent dari sebuah rule. Dua aspek penting dalam pencarian spatio-temporal association rule adalah prapemrosesan data dan algoritma pembangkitan frequent predicate. Metode prapemrosesan data berfungsi untuk memproses data sumber yang berupa data spasial dan non-spasial dengan batasan waktu dan menghasilkan data yang siap untuk di-mining. Pembangkitan frequent predicate dilakukan dengan menggunakan algoritma T-Apriori*, yaitu pengembangan algoritma T- Apriori yang diperluas untuk menangani data spatio-temporal. Selanjutnya, algoritma ini dimanfaatkan untuk mendukung proses pengambilan keputusan dengan cara mengintegrasikannya kedalam sebuah perangkat lunak SIG. Sistem ini mampu melakukan analisis data kesehatan dan demografi yang berbasis spatio-temporal dan menghasilkan knowledge dalam bentuk spatio-temporal association rule. Kata kunci: association rule, spatio-temporal data mining, frequent predicate, T-Apriori