NOTE: Your deposited thesis will only be processed when the Tuwhera Open Theses team comes back from Christmas and New Year break on January 6th, 2020. Have an awesome holiday.
    • A methodology for integrating and exploiting data mining techniques in the design of data warehouses

      Usman, M; Pears, R (IEEE, 2010)
      Data Warehousing and Data Mining are two mature disciplines in their own right. Yet, they have developed largely separate from each other, despite the fact that techniques developed for pattern recognition such as Clustering ...
    • A novel evolving clustering algorithm with polynomial regression for chaotic time-series prediction

      Widiputra, H; Kho, H; Lukas; Pears, R; Kasabov, N (Springer Berlin Heidelberg, 2009)
      Time-series prediction has been a very well researched topic in recent studies. Some popular approaches to this problem are the traditional statistical methods e.g. multiple linear regression and moving average, and neural ...
    • Accelerating multi dimensional queries in data warehouses

      Pears, R (IGI Global Publishing, 2009)
      Data Warehouses are widely used for supporting decision making. On Line Analytical Processing or OLAP is the main vehicle for querying data warehouses. OLAP operations commonly involve the computation of multidimensional ...
    • Cascade effects of load shedding in coupled networks

      Tauch, S; Liu, W; Pears, R (IEEE, 2013)
      Intricate webs of interlinked critical infrastructures such as electrical grid, telecommunication, and transportation are essential for the minimal functioning of contemporary societies and economies. Advances in Information ...
    • Dynamic Interaction Networks in modelling and predicting the behaviour of multiple interactive stock markets

      Widiputra, H; Pears, R; Serguieva, A; Kasabov, N (John Wiley & Sons, 2009)
      The behaviour of multiple stock markets can be described within the framework of complex dynamic systems. A representative technique of the framework is the dynamic interaction network (DIN), recently developed in the ...
    • Evolving integrated multi-model framework for on line multiple time series prediction

      Pears, R; Widiputra, H; Kasabov, N (Springer, 2013)
      Time series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much ...
    • FGC: an efficient constraint-based frequent set miner

      Pears, R; Kutty, S (IEEE, 2007)
      Despite advances in algorithmic design, association rule mining remains problematic from a performance viewpoint when the size of the underlying transaction database is large. The well-known a priori approach, while reducing ...
    • The incremental Fourier classifier: Leveraging the discrete Fourier transform for classifying high speed data streams

      Kithulgoda, CI; Pears, R; Naeem, MA (Elsevier, 2018)
      Two major performance bottlenecks with decision tree based classifiers in a data stream environment are the depth of the tree and the update overhead of maintaining leaf node statistics on an instance-wise basis to ensure ...
    • Integration of Data Mining and Data Warehousing: a practical methodology

      Usman, M; Pears, R (International Journal of Advancements in Computing Technology (IJACT), 2010)
      The ever growing repository of data in all fields poses new challenges to the modern analytical systems. Real-world datasets, with mixed numeric and nominal variables, are difficult to analyze and require effective visual ...
    • Integration of Data Mining and Data Warehousing: a practical methodology

      Usman, M; Pears, R (Advanced Institute of Convergence IT (AICIT), 2010)
      The ever growing repository of data in all fields poses new challenges to the modern analytical systems. Real-world datasets, with mixed numeric and nominal variables, are difficult to analyze and require effective visual ...
    • Measuring cascade effects in interdependent networks by using effective graph resistance

      Tauch, S; Liu, W; Pears, R (IEEE, 2015)
      Understanding the correlation between the underlie network structure and overlay cascade effects in the interdependent networks is one of major challenges in complex network studies. There are some existing metrics that ...
    • Mining recurrent concepts in data streams using the discrete Fourier transform

      Sripirakas, S; Pears, R (arXiv, 2014)
      In this research we address the problem of capturing recurring concepts in a data stream environment. Recurrence capture enables the re-use of previously learned classifiers without the need for re-learning while providing ...
    • Mining software metrics from Jazz

      Finlay, J; Connor, AM; Pears, R (Institute of Electrical and Electronics Engineers (IEEE), 2011)
      In this paper, we describe the extraction of source code metrics from the Jazz repository and the application of data mining techniques to identify the most useful of those metrics for predicting the success or failure of ...
    • Non-redundant rare itemset generation

      Koh, YS; Pears, R (Australian Computer Society (ACS), 2009)
      Rare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical signi cance. Research into the rare association rule mining problem has ...
    • Ontologies and machine learning systems

      Tegginmath, S; Pears, R; Kasabov, N (Springer, 2014)
      In this chapter we review the uses of ontologies within bioinformatics and neuroinformatics and the various attempts to combine machine learning (ML) and ontologies, and the uses of data mining ontologies. This is a diverse ...
    • Personalised modelling for multiple time-series data prediction

      Widiputra, H; Pears, R; Kasabov, N (Springer-Verlag, 2008)
      The behaviour of multiple stock markets can be described within the framework of complex dynamic systems (CDS). Using a global model with the Kalman Filter we are able to extract the dynamic interaction network (DIN) of ...
    • Rare association rule mining via transaction clustering

      Koh, YS; Pears, R (Australian Computer Society (ACS), 2008)
      Rare association rule mining has received a great deal of attention in the recent past. In this research, we use transaction clustering as a pre-processing mechanism to generate rare association rules. The basic concept ...