Autonomous clustering using rough set theory

This paper proposes a clustering technique that minimises the need for subjective human intervention and is based on elements of rough set theory. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency

A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system

The advancement of technology had encouraged mankind to design and create useful equipment and devices. These equipment enable users to fully utilize them in various applications. Pulp mill is one of the heavy industries that consumes large amount of electricity in its production. Due to this, any malfunction of the equipment might cause mass losses to the company. In particular, the breakdown of the generator would cause other generators to be overloaded. In the meantime, the subsequence loads will be shed until the generators are sufficient to provide the power to other loads. Once the fault had been fixed, the load shedding scheme can be deactivated. Thus, load shedding scheme is the best way in handling such condition. Selected load will be shed under this scheme in order to protect the generators from being damaged. Multi Criteria Decision Making (MCDM) can be applied in determination of the load shedding scheme in the electric power system. In this thesis two methods which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis, a series of analyses are conducted and the results are determined. Among these two methods which are AHP and TOPSIS, the results shown that TOPSIS is the best Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill system. TOPSIS is the most effective solution because of the highest percentage effectiveness of load shedding between these two methods. The results of the AHP and TOPSIS analysis to the pulp mill system are very promising

Positive region: An enhancement of partitioning attribute based rough set for categorical data

Datasets containing multi-value attributes are often involved in several domains, like pattern recognition, machine learning and data mining. Data partition is required in such cases. Partitioning attributes is the clustering process for the whole data set which is specified for further processing. Recently, there are already existing prominent rough set-based approaches available for group objects and for handling uncertainty data that use indiscernibility attribute and mean roughness measure to perform attribute partitioning. Nevertheless, most of the partitioning attribute methods for selecting partitioning attribute algorithm for categorical data in clustering datasets are incapable of optimal partitioning. This indiscernibility and mean roughness measures, however, require the calculation of the lower approximation, which has less accuracy and it is an expensive task to compute. This reduces the growth of the set of attributes and neglects the data found within the boundary region. This paper presents a new concept called the "Positive Region Based Mean Dependency (PRD)”, that calculates the attribute dependency. In order to determine the mean dependency of the attributes, that is acceptable for categorical datasets, using a positive region-based mean dependency measure, PRD defines the method. By avoiding the lower approximation, PRD is an optimal substitute for the conventional dependency measure in partitioning attribute selection. Contrary to traditional RST partitioning methods, the proposed method can be employed as a measure of data output uncertainty and as a tailback for larger and multiple data clustering. The performance of the method presented is evaluated and compared with the algorithmes of Information-Theoretical Dependence Roughness (ITDR) and Maximum Indiscernible Attribute (MIA)

Gabor Filter and Rough Clustering Based Edge Detection

This paper introduces an efficient edge detection method based on Gabor filter and rough clustering. The input image is smoothed by Gabor function, and the concept of rough clustering is used to focus on edge detection with soft computational approach. Hysteresis thresholding is used to get the actual output, i.e. edges of the input image. To show the effectiveness, the proposed technique is compared with some other edge detection methods.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201

Fast attribute selection based on the rough set boundary region

The problem of clustering exists in numerous fields such as bioinformatics, data mining, and the recognition of patterns. The function of techniques is to suitably select the best attribute from numerous contending attribute(s). RST-based approaches for definite data has gained significant attention, but cannot select clustering attributes for optimum performance. In this paper, the focus is on the processes that exhibit a similar degree of results to an identical attribute value. First, the MIA algorithm was identified as the supplement to the MSA algorithm, which experiences set approximation. Second, the proposition that MIA accomplishes lesser computational complexity through the indiscernibility relation measurement was highlighted. This observation is ascribed to the relationship between various attributes, which is markedly similar to those induced by others. Based on the fact that the size of the attribute domain is relatively small, the selection of such an attribute under such circumstances is problematic. Failure to choose the most suitable clustering attribute is challenging and the set is defined rather than computing the relative mean where it can only be implemented with a distinctive category of the information system, as illustrated with an example. Lastly, a substitute method for selecting a clustering attribute-based RST using Mean Dependency degree attribute(s) (MMD) was proposed. This involved selecting the maximum value of a mean attribute(s) as a clustering attribute through a considerable targeting procedure for the rapid selection of an attribute to settle the instability in selecting clustering attributes. Thus, the comparative performance of the selected clustering attributes-based RST techniques MSA and MIA was conducted

AN INDISCERNIBILITY APPROACH FOR PRE PROCESSING OF WEB LOG FILES

World Wide Web has a spectacular growth not only in terms of the number of websites and volume of information, but also in terms of the number of visitors. Web log files contain tremendous information about the user traffic and behavior. A large amount of pre processing is required for eliminating the noise and is one of the challenging tasks in web usage mining. This paper proposes an indiscernibility approach in rough set theory for pre processing of web log files