Research of the Optimization of a Data Mining Algorithm Based on an Embedded Data Mining System

Abstract At present most of the data mining systems are independent with respect to the database system, and data loading and conversion take much time. The running time of the algorithms in a data mining process is also long. Although some optimized algorithms have improved it in different aspects, they could not improve the efficiency to a large extent when many duplicate records are available in a database. Solving the problem of improving the efficiency of data mining in the presence of many coinciding records in a database, an Apriori optimized algorithm is proposed. Firstly, a new concept of duplication and use is suggested to remove and count the same records, in order to generate a new database of a small size. Secondly, the original database is compressed according to the users’ requirements. At last, finding the frequent item sets based on binary coding, strong association rules are obtained. The structure of the data mining system based on an embedded database has also been designed in this paper. The theoretical analysis and experimental verification prove that the optimized algorithm is appropriate and the algorithm application in an embedded data mining system can further improve the mining efficiency.

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A model for data mining system in financial crisis management based on data warehouse concept

Computer Science and Information Systems ◽

10.2298/csis0501043k ◽

2005 ◽

Vol 2 (1) ◽

pp. 43-62

Author(s):

Ljiljana Kascelan ◽

Dragana Becejski-Vujaklija

Keyword(s):

Data Mining ◽

Financial Crisis ◽

Crisis Management ◽

Data Warehouse ◽

Decision Processes ◽

Data Mining Algorithm ◽

Mining System ◽

Business Decision ◽

Data Mining System ◽

Target Data

This paper deals with identification and analyses of business decision processes in financial crisis management and appropriate relational data warehouse design for this processes. Also, here a model for financial crises symptoms is proposed and a data mining algorithm for automatic detection of those symptoms is developed. Finally, paper presents the concept for realization of target data mining system, using Oracle tools.

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Secure Multi-User k-Means Clustering Based on Encrypted IoT Data

Computer and Information Science ◽

10.5539/cis.v12n2p35 ◽

2019 ◽

Vol 12 (2) ◽

pp. 35

Author(s):

Yanling Li ◽

Chuansheng Wang ◽

Qi Wang ◽

Jieling Dai ◽

Yushan Zhao

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Theoretical Analysis ◽

Knowledge Discovery ◽

Data Mining Algorithm ◽

Personal Privacy ◽

Encrypted Data ◽

Mining Algorithm ◽

Public Keys ◽

Semantic Security

IoT technology collects information from a lot of clients, which may relate to personal privacy. To protect the privacy, the clients would like to encrypt the raw data with their own keys before uploading. However, to make use of the information, the data mining technology with cloud computing is used for the knowledge discovery. Hence, it is an emergent issue of how to effectively performing data mining algorithm on the encrypted data. In this paper, we present a k-means clustering scheme with multi-user based on the IoT data. Although, there are many privacy-preserving k-means clustering protocols, they rarely focus on the situation of encrypting with different public keys. Besides, the existing works are inefficient and impractical. The scheme we propose in this paper not only solves the problem of evaluation on the encrypted data under different public keys but also improves the efficiency of the algorithm. It is semantic security under the semi-honest model according to our theoretical analysis. At last, we evaluate the experiment based on a real dataset, and comparing with previous works, the result shows that our scheme is more efficient and practical.

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