Efficient Data Mining Classification Technique Essay

Efficient Data Mining Classification Technique - Essay Example Therefore, the concept-evolution problem would be effectively addressed in this thesis along with data reduction and class balancing issues. This research project aims to study the shortcomings of existing novel class detection, data reduction, and class balancing data mining techniques in terms of their accuracy, efficiency, and applicability to real life applications of multi streaming data. The aim of the research is also to provide alternate solutions to overcome those drawbacks. My thesis aims to propose a general model and algorithm that will be tested on synthetic data and well known real data sets e.g. KDD Cup 99 network intrusion detection (KDD), Auslan [Kad02], and EMG [Kol05]. Classification, clustering, and aggregation are some of the data mining hot topics that are of extreme value in all engineering and scientific areas, such as, biological, physical and bio-medical sciences. Diversified nature of escalated data along with its composite aspects and multiple autonomous sources is a major issue in data mining that leads to the need for the development of real life applications. The motivation behind this study is offered in the following paragraphs: The first issue the thesis is going to address is that of evolving data, which represents a challenge for classification. The effective and efficient methods are needed by the growing and dynamic data streams, which are considerably different from the static data mining methods. The concept drift and infinite length are considered to be the well-studied features of data streams. Across data stream mining, to address the infinite length [Fan04] and concept-drift[Cha07][Kol05] [Wan03], diverse methods have been suggested in the literature. Yet, the data streams have two another challenging characteristics, known as, feature-evolution and concept-evolution, which are ignored by the present methods.