Titlebook: Advances in Knowledge Discovery and Data Mining, Part II; 16th Pacific-Asia Co Pang-Ning Tan,Sanjay Chawla,James Bailey Conference proceedi

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書目名稱Advances in Knowledge Discovery and Data Mining, Part II影響因子(影響力)




書目名稱Advances in Knowledge Discovery and Data Mining, Part II影響因子(影響力)學科排名




書目名稱Advances in Knowledge Discovery and Data Mining, Part II網(wǎng)絡(luò)公開度




書目名稱Advances in Knowledge Discovery and Data Mining, Part II網(wǎng)絡(luò)公開度學科排名




書目名稱Advances in Knowledge Discovery and Data Mining, Part II被引頻次




書目名稱Advances in Knowledge Discovery and Data Mining, Part II被引頻次學科排名




書目名稱Advances in Knowledge Discovery and Data Mining, Part II年度引用




書目名稱Advances in Knowledge Discovery and Data Mining, Part II年度引用學科排名




書目名稱Advances in Knowledge Discovery and Data Mining, Part II讀者反饋




書目名稱Advances in Knowledge Discovery and Data Mining, Part II讀者反饋學科排名

發(fā)芽

和藹

Philosophical Ethics and Practical Ethics,ously arriving. It is a known fact that in high-dimensional datasets, not all features are critical for training a classifier. To improve the performance of data stream classification, we propose an algorithm called HEFT-Stream (.eterogeneous .nsemble with .eature drif. for Data .) that incorporates

粗俗人

The Skeptical Critique of Clinical Ethics,pect, the Intrusion Detection Systems .IDS. are intended to protect information systems against intrusions. However, IDS are plugged with several problems that slow down their development, such as low detection accuracy and high false alarm rate. In this paper, we introduce a new IDS, called OMC-IDS

鼓掌

溫室

The Process of Ethics Consultation,Mining approach. The assignment of high weights to important items enables rules that express relationships between high weight items to be ranked ahead of rules that only feature less important items. Most previous research to weight assignment has used subjective measures to assign weights and are

ORBIT

Genetics: Nurses Roles and Responsibilitiesssion (AE) events which are caused by damage. Then we propose a novel method for mining patterns from the numerical data such as AE. The proposed method extracts patterns of two clusters considering co-occurrence between clusters and similarity within each cluster at the same time. In addition, we u

開玩笑

Ben Benjamin,Aimee Milliken,Pamela Grace. Nevertheless, a main problem is related to their very high number and to the low quality of several mined rare patterns. In order to overcome these limits, we propose to integrate the correlation measure . aiming at only mining the set of rare . patterns. A characterization of the resulting set is

博愛家

intelligible