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The development of database technology has solved the memory and retrieval of substantive data, but the biomedicine database existing the phenomenon of “data rich, information poor”. In order to solve the problem of Knowledge Discovery in Database, great importance has been continuously attached to the data mining. In this paper, we elaborate the Particularities and Key issues of data mining in biomedicine,...
One of the most popular machine learning algorithms, ANN (Artificial Neural Network) has been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. Data mining has extensive and significant applications in a large variety of areas. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks...
The way of collecting sensor data will face a revolution when newly developing technology of sensor network will become fully functional. The program/stack memory and the battery life of sensor nodes are not suitable for complex data mining in runtime. Effective data mining can be implemented on the central base station, where the computational power is not generally constrained. Real-world sensor...
When designing a neural or fuzzy system, a careful preprocessing of the database is of utmost importance in order to produce a trustable system. In function approximation applications, when a functional relationship between input and output variables is supposed to exist, the presence of data where the similar set of input variables is associated to very different values of the output is not always...
Our goal is to predict the performance of multi-node systems consisting of identical processing nodes based on single node profiles. The performance of multi-node systems significantly depends on the amount of inter-node communication. Therefore, we built an analytical model of the communication amount, i.e., the number of transfers of cached copies, on multi-node systems with coherence mechanisms...
Many speech and language related techniques employ models that are trained using text data. In this paper, we introduce a novel method for selecting optimized training sets from text databases. The coverage of the subset selected for training is optimized using hierarchical clustering and the generalized Levenshtein distance. The validity of the proposed subset optimization technique is verified in...
This paper has analysed the a priori algorithm performance, and has pointed out performance bottleneck question of the a priori algorithm. Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy respectively. There is a paradox between efficiency and accuracy. In order to resolve to this conflict, a novel algorithm based on probability estimate...
Intrusion detection system (IDS) is an effective tool that can help to prevent unauthorized access to network resources. A good intrusion detection system should have higher detection rate and lower false positive. A new classification system using Jordan/Elman (J/L) neural network for ID is proposed to detect intrusions from normal connections with satisfactory detection rate and false positive....
The databases of real world contains a huge volume of data and among them there are hidden piles of interesting relations that are actually very hard to find out. The knowledge discovery databases (KDD) appear as a possible solution to find out such relations aiming at converting information into knowledge. However, not a data presented in the bases are useful to a KDD. Usually, data are processed...
Many results in the literature indicate that the incremental approach to association mining leads to gain regarding the time needed to obtain the rules, but there is no evaluation about their quality, compared to non-incremental algorithms. This paper presents the comparison of usage of two typical algorithms representing each approach: APriori and ZigZag. Execution time clearly shows the advantage...
Skyline queries have been actively studied lately as they can effectively identify interesting candidate objects with low formulation overhead. In particular, this paper studies supporting skyline queries for the uncertain data with "maybe" uncertainty, e.g., automatically extracted data. Prior skyline works on uncertain data assumes that every possible value for an uncertain object can...
Extracting schemas plays an important role in XML database management. Since XML recently appear largely, MVD is introduced into XML research. MVD can be redefined according to the range of constraints of XML. This paper also proposes a novel algorithm to solve the problem that non-integrity data of XML influence on finding the MVD of XML. The semantics information is important to the design of XML...
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