Data Mining - Concepts and Techniques.

https://opus.lib.uts.edu.au/bitstream/10453/114834/4/OCC-90253_AM.pdf

A review of clustering techniques and developments

It is well known that the photon is only a quanta of electromagnetic radiation However, there are many myths around the photon in contemporary physics, for example, the photon loses energy when traveling through space The article explains the basic features of the photon, such as wave-particle duality, the relation between a continuous electromagnetic wave and a quanta, the interaction of electric and magnetic fields, space of photon, speed of light

Nature of Photon

斜向妥当化(Cross-Validation)の必要と方法

An automated detection and classification of citrus plant diseases using image processing techniques: A review

In the arena of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a computational intelligence discipline which has emerged as a valuable tool for data analysis, new knowledge discovery and autonomous decision making. The raw, unlabeled data from the large volume of dataset can be classified initially in an unsupervised fashion by using cluster analysis i.e. clustering the assignment of a set of observations into clusters so that observations in the same cluster may be in some sense be treated as similar. The outcome of the clustering process and efficiency of its domain application are generally determined through algorithms. There are various algorithms which are used to solve this problem. In this research work two important clustering algorithms namely centroid based K-Means and representative object based FCM (Fuzzy C-Means) clustering algorithms are compared. These algorithms are applied and performance is evaluated on the basis of the efficiency of clustering output. The numbers of data points as well as the number of clusters are the factors upon which the behaviour patterns of both the algorithms are analyzed. FCM produces close results to K-Means clustering but it still requires more computation time than K-Means clustering. Keywords—clustering; k-means; fuzzy c-means; time complexity

/pdf/comparative-analysis-of-k-means-and-fuzzy-c-means-algorithms-3dh1nvc16z.pdf

Comparative Analysis of K-Means and Fuzzy C- Means Algorithms

With the objective of presenting qualitative software system most of the software producers have endeavoured and infact this is their main purpose. Software quality is having multi-dimensional content which may be distinguished and measured easily. To be specific, with the idea of determining the multidimensional content in a more exact pattern various qualitative models have been presented by virtue of which different aspects of this matter have been attempted to be investigated properly. Practically as because there are existences of different models which have used different expressions the comprehension of this basic content have become to some extent hard or difficult. Attempts have been made in this particular paper to introduce some models that have been mentioned and more clearly analysis the qualitative characteristics and side-by-side determine the software quality along with the analytical comparison of these models. This paper may serve the purpose of a reference for investigating software quality and its related models.

Comparison of Software Quality Models: An Analytical Approach

Development of software that may be encouraging for the developers and yield more customer satisfaction in lesser time and cost requires early prediction of defects lying already in the software system. Development of a defect-free and reliable software system involves conducting series of test cases which is actually a time consuming and cost oriented exercise. It requires framing a defect prediction model applying effective technique with suitable defect prediction performance measures that may be empirically validated for ensuring relevance to software organizations. Although series of defect prediction models have been developed using various classifiers and different techniques on defect datasets but those models were not at all fault-free and fully effective to achieve the goal. As such, it has become pertinent to set up an empirical framework and develop a newer Nonlinear Manifold Detection (NMD) Model along with various machine learning approaches for prediction of defects in software in most accurate manner. The new NMD Model ventured in identifying the attributes which are best and in that process all the unwanted, redundant and undesired attributes were eliminated. In this model, critical analysis and comparison with other Feature selection approaches have been made and the results have showed that NMD Model is more accurate and effective in predicting software defects. The prediction performance of various machine learning approaches have actually been compared by using measures like Accuracy, MAE, RMSE, AUC and they have also been tested statistically by use of Friedman and Nemenyi test. The experiment finally proved that NMD Model is more effective, significant and better result-oriented in terms of accuracy than other defect prediction approaches.

A Novel Model Based on Nonlinear Manifold Detection for Software Defect Prediction

Any particular study on software quality with all desirable attributes of software products can be treated as complete and perfect provided it is defective. Defects continue to be an emerging problem that leads to failure and unexpected behaviour of the system. Prediction of defect in software system in the initial stage may be favourable to a great extend in the process of finding out defects and making the software system efficient, defect-free and improving its over-all quality. To analyze and compare the work done by the researchers on predicting defects of software system, it is necessary to have a look on their varied work. The most frequently used methodologies for predicting defects in the software system have been highlighted in this paper and it has been observed that use of public datasets were considerably more than use of private datasets. On the basis of over-all findings, the key analysis and challenging issues have been identified which will help and encourage further work in this field with application of newer and more effective methodologies.

Soumi Ghosh

Papers

Comparative Analysis of K-Means and Fuzzy C- Means Algorithms

Comparison of Software Quality Models: An Analytical Approach

A Novel Model Based on Nonlinear Manifold Detection for Software Defect Prediction

Predicting Defect of Software System

A Nonlinear Manifold Detection based Model for Software Defect Prediction