7496796
Evolutionary time series segmentation for stock data mining
Stock data in the form of multiple time series are difficult to process, analyze and mine. However, when they can be transformed into meaningful symbols like technical patterns, it becomes easier. Most recent work on time series queries concentrates only on how to identify a given pattern from a time series. Researchers do not consider the problem of identifying a suitable set of time points for segmenting the time series in accordance with a given set of pattern templates (e.g., a set of technical patterns for stock analysis). On the other hand, using fixed length segmentation is a primitive approach to this problem; hence, a dynamic approach (with high controllability) is preferred so that the time series can be segmented flexibly and effectively according to the needs of users and applications. In view of the fact that such a segmentation problem is an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary time series segmentation algorithm. This approach allows a sizeable set of stock patterns to be generated for mining or query. In addition, defining the similarity between time series (or time series segments) is of fundamental importance in fitness computation. By identifying perceptually important points directly from the time domain, time series segments and templates of different lengths can be compared and intuitive pattern matching can be carried out in an effective and efficient manner. Encouraging experimental results are reported from tests that segment the time series of selected Hong Kong stocks.
33759649
A New Sequential Mining Approach to XML Document Similarity Computation
There exist several methods to measuring the structural similarity among XML documents. The data mining approach seems to be a novel, interesting and promising one. In view of the deficiencies encountered by ignoring the hierarchical information in encoding the paths for mining, we propose a new sequential pattern mining scheme for XML document similarity computation. It makes use of the hierarchical information to computing the document structural similarity. In addition, it includes a post-processing step to reuse the mined patterns to estimate the similarity of unmatched elements so that another metric to qualify the similarity between XML documents can be introduced. Encouraging experimental results were obtained and reported.
5672195
Efficient Huffman Decoding
We first present a memory-efficient array data structure to represent the Huffman tree. We then present a fast Huffman decoding algorithm.
5140341
Actor centrality correlates to project based coordination
In this study, we draw on network centrality concepts and coordination theory to understand how project team members interact when working towards a common goal. A text-mining application based on the constructs of coordination theory was developed to measure the coordinative activity of each employee. Results show that high network centrality is correlated with the ability of an actor to coordinate actions of others in a project group. Furthermore, highly centralised actors coordinate better than others. In conclusion, we suggest implications of appropriate network structure for supporting organisational coordination more effectively and efficiently.