DisClose: Discovering Colossal Closed Itemsets via a Memory Efficient Compact Row-Tree
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چکیده
3:00 PM Session 1 DisClose: Discovering Colossal Closed Itemsets via a Memory Efficient Compact Row-Tree Nurul F. Zulkurnain, David J. Haglin and John A. Keane Triangular Kernel Nearest-Neighbor-based Clustering Algorithm for Discovering True Clusters Aina Musdholifah and Siti Zaiton Mohd Hashim An Improved Genetic Clustering Algorithm for Categorical Data Hongwu Qin, Xiuqin Ma, Tutut Herawan, and Jasni Mohamad Zain Instance-Ranking: A new perspective to consider the instance dependency for classification Xin Xia, Xiaohu Yang, Shanping Li and Chao Wu 3:30 PM Coffee Break
منابع مشابه
DisClose : discovering colossal closed itemsets from high dimensional datasets via a compact row-tree
Data mining is an essential part of knowledge discovery, and performs the extraction of useful information from a collection of data, so as to assist human beings in making necessary decisions. This thesis describes research in the field of itemset mining, which performs the extraction of a set of items that occur together in a dataset, based on a user specified threshold. Recent focus of items...
متن کاملCHARM: An Efficient Algorithm for Closed Itemset Mining
The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper we present CHARM, an efficient algorithm for mining all frequent closed itemsets. It enumerates closed sets using a dual itemset-tidset search tree, using an efficient hybrid search that skips many levels. It ...
متن کاملEFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets
Discovering high-utility temsets in transaction databases is a popular data mining task. A limitation of traditional algorithms is that a huge amount of high-utility itemsets may be presented to the user. To provide a concise and lossless representation of results to the user, the concept of closed high-utility itemsets was proposed. However, mining closed high-utility itemsets is computational...
متن کاملCLAIM: An Efficient Method for Relaxed Frequent Closed Itemsets Mining over Stream Data
Recently, frequent itemsets mining over data streams attracted much attention. However, mining closed itemsets from data stream has not been well addressed. The main difficulty lies in its high complexity of maintenance aroused by the exact model definition of closed itemsets and the dynamic changing of data streams. In data stream scenario, it is sufficient to mining only approximated frequent...
متن کاملCLOLINK: An Adapted Algorithm for Mining Closed Frequent Itemsets
Mining of the complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of closed frequent itemsets, which results in a much smaller number of itemsets. Methods for efficient mining of closed frequent itemsets have been studied extensively by many researchers using various strategies to prove their efficiencies such as Aprio...
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تاریخ انتشار 2012