In data mining framework, for proficient data examination recent researchers utilized branch-and-bound methods such as seriation, clustering, and feature selection. Conventional cluster search was completed with diverse partitioning schemes to optimize the cluster pattern. Considering image data, partitioning approaches seems to be computationally complex due to large data size, and uncertainty of number of clusters. Recent work presented a new version of branch and bound model called model selection problem, handles the clustering issues more efficiently. The existing work deployed spatially coherent sampling for generating cluster parameter candidates. But if the problem-specific bounds and/or added heuristics in the data points of the domain area get surmounted, memory overheads, specific model selection, and uncertain data points cause various clustering abnormalities. To overcome the above mentioned issues, we plan to present an Optimal Model-Selection Clustering for image data point analysis in the context of knowledge and data discovery in highly dense data points with more uncertainty. In this work, we are going to analyze the model selection clustering which is first initiated through the process of heuristic training sequences on image data points and appropriates the problem-specific characteristics. Heuristic training sequences will generate and test a set of models to determine whether the model is matched with the characteristics of the problem or not. Through the process of heuristic training sequences, we efficiently perform the model selection criteria. An experimental evaluation is conducted on the proposed model selection clustering for image data point using heuristic approach (MSCHA) with real and synthetic data sets extracted from research repositories (UCI) and performance of the proposed MSCHA is measured in terms of Data point density, Model-Selection Criteria, Cluster validity.
Image data points mining is concerned with the extraction of knowledge relationship among image data and other patterns inherent in the images. Taxonomy-Aware Catalog Integration (TCI) processing step ensures that the master taxonomy Rule-based Multivariate Text Feature Selection (RMTFS) method takes into consideration both the semantic information and the syntactic relationships between n-gram features. In realizing the relationship between the attributes, the Decision Tree-based Label Feature Classifier (DTLFC) mechanism is proposed. In the initial step in the DTLFC mechanism, pixel-wise image feature points are extracted and the same is converted into a table of a database. The feature descriptor thus formed by combining a features set and a specific pixel's label is represented by a tuple of the transformed database table and helps in the generation of the decision tree using a given image points data set in constructing a pixel-wise image processing model. Both experimental and theoretical analysis demonstrate that the DTLFC mechanism can attain a very efficient and effective level of classification on image data points. Compared with the TCI and RMTFS methods, the DTLFC mechanism creates an effective feature classifier in terms of filtering efficiency, classification accuracy, processing time, computational cost, scalability, and the intensity rate of dominant attributes.
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