46460164
Multilevel fuzzy relational systems: structure and identification
Abstract Existing fuzzy relational equations (FRE) typically possess an evident single-level structure, where no consequence part of the rule being modeled, is used as a fact to another rule. Corresponding to multistage fuzzy reasoning, a natural extension of traditional fuzzy relational systems (FRS) is to introduce some intermediate levels of processing governed by enhanced FRE's so that the structure resulted becomes multilevel or multistage. Three basic multilevel FRS structures, namely, incremental, aggregated, and cascaded, are considered in this paper and they correspond to different reasoning mechanisms being frequently used by human beings in daily life. While the research works on multilevel FRS are sparse and our ability to solve a system of multilevel FRE's in a purely analytical manner is very limited, we address the identification problem from an optimization approach and introduce three fuzzy neural models. The proposed models consist of single-level FRS modules that are arranged in different hierarchical manners. Each module can be realized by Lin and Lee's fuzzy neural model for implementing the Mamdani fuzzy inference. We have particularly addressed the problem of how to distribute the input variables to different (levels of) relational modules for the incremental and aggregated models. In addition, the new models can learn a complete multistage fuzzy rule set from stipulated data pairs using structural and parameter learning. The effectiveness of the multilevel models has been demonstrated through various benchmarking problems. It can be generally concluded that the new models are distinctive in learning, generalization, and robustness.
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.
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.
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.