Hash-tree is an important data structure used in Apriori-like algorithms for mining frequent itemsets.However, there is no study so far to guarantee the hash-tree could be built successfully every time. In this paper,...Hash-tree is an important data structure used in Apriori-like algorithms for mining frequent itemsets.However, there is no study so far to guarantee the hash-tree could be built successfully every time. In this paper, wepropose a static method and a dynamic one to build the hash-tree. In the two methods, it is easy to decide the size ofhash-table, hash function and the number of itemsets stored in each leaf-node of hash-tree, and the methods ensurethat the hash-tree is built successfully in any cases.展开更多
In this paper, we introduce a new resolution--Bullant to develop Web application, which represents userinterfaces and business logic on the server while the clients just display the interfaces and accept users' in...In this paper, we introduce a new resolution--Bullant to develop Web application, which represents userinterfaces and business logic on the server while the clients just display the interfaces and accept users' inputs. TheWeb server can keep the data as persistent objects in no demand of external database ,support types of devices such asPC, PDA, mobile phone etc. in wired/wireless mode through the lowest bandwidth of 9.6K/s and is linear scalablewith Zero Friction Engine. It's a thoroughly new resolution compared with the others.展开更多
Mining frequent itemsets from large databases has played an essential role inmany data mining tasks. It is also important to maintain the discovered frequent itemsets forthese data mining tasks when the database is up...Mining frequent itemsets from large databases has played an essential role inmany data mining tasks. It is also important to maintain the discovered frequent itemsets forthese data mining tasks when the database is updated. All algorithms proposed so far for the maintenance of discovered frequent itemsets are only performed with a fixed minimum support,which is the same as that used to obtain the discovered frequent itemsets. That is, users cannot change the minimum support even if the new results are unsatisfactory to the users. In thispaper two new complementary algorithms, FMP (First Maintaining Process) and RMP (Repeated Maintaining Process), are proposed to maintain discovered frequent itemsets in the case that new transaction data are added to a transaction database. Both algorithms allow users to change theminimum support for the maintenance processes. FMP is used for the first maintaining process, andwhen the result derived from the FMP is unsatisfactory, RMP will be performed repeatedly untilsatisfactory results are obtained. The proposed algorithms re-use the previous results to cut downthe cost of maintenance. Extensive experiments have been conducted to assess the performance of the algorithms. The experimental results show that the proposed algorithms are very resultful compared with the previous mining and maintenance algorithms for maintenance of discovered frequent itemsets.展开更多
文摘Hash-tree is an important data structure used in Apriori-like algorithms for mining frequent itemsets.However, there is no study so far to guarantee the hash-tree could be built successfully every time. In this paper, wepropose a static method and a dynamic one to build the hash-tree. In the two methods, it is easy to decide the size ofhash-table, hash function and the number of itemsets stored in each leaf-node of hash-tree, and the methods ensurethat the hash-tree is built successfully in any cases.
文摘In this paper, we introduce a new resolution--Bullant to develop Web application, which represents userinterfaces and business logic on the server while the clients just display the interfaces and accept users' inputs. TheWeb server can keep the data as persistent objects in no demand of external database ,support types of devices such asPC, PDA, mobile phone etc. in wired/wireless mode through the lowest bandwidth of 9.6K/s and is linear scalablewith Zero Friction Engine. It's a thoroughly new resolution compared with the others.
文摘Mining frequent itemsets from large databases has played an essential role inmany data mining tasks. It is also important to maintain the discovered frequent itemsets forthese data mining tasks when the database is updated. All algorithms proposed so far for the maintenance of discovered frequent itemsets are only performed with a fixed minimum support,which is the same as that used to obtain the discovered frequent itemsets. That is, users cannot change the minimum support even if the new results are unsatisfactory to the users. In thispaper two new complementary algorithms, FMP (First Maintaining Process) and RMP (Repeated Maintaining Process), are proposed to maintain discovered frequent itemsets in the case that new transaction data are added to a transaction database. Both algorithms allow users to change theminimum support for the maintenance processes. FMP is used for the first maintaining process, andwhen the result derived from the FMP is unsatisfactory, RMP will be performed repeatedly untilsatisfactory results are obtained. The proposed algorithms re-use the previous results to cut downthe cost of maintenance. Extensive experiments have been conducted to assess the performance of the algorithms. The experimental results show that the proposed algorithms are very resultful compared with the previous mining and maintenance algorithms for maintenance of discovered frequent itemsets.
