Root-Cause and Defect Analysis based on a Fuzzy Data Mining Algorithm

Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered list of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.

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A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488505003722 ◽

2005 ◽

Vol 13 (06) ◽

pp. 633-652 ◽

Cited By ~ 11

Author(s):

R. B. V. SUBRAMANYAM ◽

A. GOSWAMI

Keyword(s):

Data Mining ◽

Real World ◽

Uncertain Data ◽

Sequential Patterns ◽

Data Mining Algorithm ◽

Fuzzy Data ◽

Incremental Mining ◽

Data Mining Algorithms ◽

Mining Algorithm ◽

Real World Applications

In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative databases is proposed. Proposed algorithm called IQSP algorithm uses the fuzzy grid notion to generate fuzzy sequential patterns validated on the updated database containing the transactions in the original database and in the incremental database. It uses the information about sequential patterns that are already mined from original database and avoids start-from-scratch process. Also, it minimizes the number of candidates to check as well as number of scans to original database by identifying the potential sequences in incremental database.

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Mining Frequent Fuzzy Grids in Dynamic Databases with Weighted Transactions and Weighted Items

Journal of Information & Knowledge Management ◽

10.1142/s0219649206001487 ◽

2006 ◽

Vol 05 (03) ◽

pp. 243-257

Author(s):

R. B. V. Subramanyam ◽

A. Goswami

Keyword(s):

Data Mining ◽

Association Rule ◽

Initial Level ◽

Data Mining Algorithm ◽

Fuzzy Data ◽

Incremental Mining ◽

Mining Algorithm ◽

The Status ◽

Dynamic Databases ◽

Mining Algorithms

Incremental mining algorithms that derive the latest mining output by making use of previous mining results are attractive to business organisations. In this paper, a fuzzy data mining algorithm for incremental mining of frequent fuzzy grids from quantitative dynamic databases is proposed. It extends the traditional association rule problem by allowing a weight to be associated with each item in a transaction and with each transaction in a database to reflect the interest/intensity of items and transactions. It uses the information about fuzzy grids that are already mined from original database and avoids start-from-scratch process. In addition, we deal with "weights-of-significance" which are automatically regulated as the incremental databases are evolved and implant themselves in the original database. We maintain "hopeful fuzzy grids" and "frequent fuzzy grids" and our algorithm changes the status of the grids which have been discovered earlier so that they reflect the pattern drift in the updated quantitative databases. Our heuristic approach avoids maintaining many "hopeful fuzzy grids" at the initial level. The algorithm is illustrated with one numerical example and demonstration of experimental results are also incorporated.

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Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data

Advances in Fuzzy Systems ◽

10.1155/2016/1479692 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13

Author(s):

Munish Saini ◽

Sandeep Mehmi ◽

Kuljit Kaur Chahal

Keyword(s):

Data Mining ◽

Time Series ◽

Open Source ◽

Open Source Software ◽

Time Series Data ◽

Series Data ◽

Software Project ◽

Data Mining Algorithm ◽

Fuzzy Data ◽

Mining Algorithm

Source code management systems (such as Concurrent Versions System (CVS), Subversion, and git) record changes to code repositories of open source software projects. This study explores a fuzzy data mining algorithm for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. The idea to choose fuzzy data mining algorithm for time series data is due to the stochastic nature of the open source software development process. Commit activity of an open source project indicates the activeness of its development community. An active development community is a strong contributor to the success of an open source project. Therefore commit activity analysis along with the trend and regularity analysis for commit activity of open source software project acts as an important indicator to the project managers and analyst regarding the evolutionary prospects of the project in the future.

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Construction of Social Security Fund Cloud Audit Platform Based on Fuzzy Data Mining Algorithm

Complexity ◽

10.1155/2021/9939454 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yangting Huai ◽

Qianxiao Zhang

Keyword(s):

Data Mining ◽

Social Security ◽

Data Mining Algorithm ◽

Fuzzy Data ◽

Social Security Fund ◽

Mining Algorithm ◽

The Neural Network ◽

Security Fund ◽

Security Funds ◽

Cloud Audit

Guided by the theories of system theory, synergetic theory, and other disciplines and based on fuzzy data mining algorithm, this article constructs a three-tier social security fund cloud audit platform. Firstly, the article systematically expounds the current situation of social security fund and social security fund audit, such as the technical basis of cloud computing and data mining. Combined with the actual work, the necessity and feasibility of building a cloud audit platform for social security funds are analyzed. This article focuses on the construction of the cloud audit platform for social security funds. The general idea of using fuzzy data mining algorithm to build the social security fund audit cloud platform is to compress the knowledge contained in a large number of data into the weights between nodes and optimize the weights through the learning of the neural network system. Through the optimization function, the information contained in the neural network is stored in a few weights as far as possible. The main information is further highlighted by network clipping and removing weights that have little impact on the output.

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