An algorithm for fast mining top-rank-k frequent patterns based on node-list data structure

Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and post-order transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_TopK uses the minimal support threshold for pruning strategy to guarantee that each pattern in the top-rank-k table is really frequent and this further improves the efficiency. Experiments are conducted to compare FP_TopK with iNTK and BTK on four datasets. The results show that FP_TopK achieves better performance

Comparison of deposition methods of ZnO thin film on flexible substrate

This paper reports the effect of the different deposition methods towards the ZnO nanostructure crystal quality and film thickness on the polyimide substrate. The ZnO film has been deposited by using the spray pyrolysis technique, sol-gel and RF Sputtering. Different methods give a different nanostructure of the ZnO thin film. Sol gel methods, results of nanoflowers ZnO thin film with the thickness of thin film is 600nm. It also produces the best of the piezoelectric effect in term of electrical performance, which is 5.0 V and 12 MHz of frequency which is higher than other frequency obtained by spray pyrolysis and RF sputtering

Efficient mining of frequent patterns using ascending frequency ordered prefix-tree

Mining frequent patterns, including mining frequent closed patterns or maximal patterns, is a fundamental and important problem in data mining area. Many algorithms adopt the pattern growth approach, which is shown to be superior to the candidate generate-and-test approach, especially when long patterns exist in the datasets. In this paper, we identify the key factors that influence the performance of the pattern growth approach, and optimize them to further improve the performance. Our algorithm uses a simple while compact data structure-ascending frequency ordered prefix-tree (AFOPT) to store the conditional databases, in which we use arrays to store single branches to further save space. The AFOPT structure is traversed in top-down depth-first order. Our analysis and experiment results show that the combination of the top-down traversal strategy and the ascending frequency order achieves significant performance improvement over previous works

A geographic knowledge discovery approach to property valuation

This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely data driven and does not require previous knowledge of the area applied. To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm. For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the investigation of relationships between spatial entities beyond first degree connectivity. A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality