Re-engineering Technology: Date Mining for Clubs

With increasing competition and more demanding members, clubs need a tool to help them belter attract and retain members and predict their behavior. Data mining is such a tool. This article presents an overview of how data warehousing, data marting, and data mining can provide the foundation on which clubs can build strategies to outsmart competitors, build Ioyalty identify new members, and lower costs.

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Profiling The Campus Recruiter At A Four-Year Hospitality Program

Profiling the Campus Recruiter At a Four-Year Hospitality Program, is a written profile, supported by anecdotal rather than stridently empirical evidence, by Al lzzolo, Assistant Professor, College of Hotel Administration, University of Nevada, Las Vegas. “Each year major chain corporations as well as single unit companies interview hospitality students throughout the country. A study conducted at the University of Nevada, Las Vegas, was designed to profile the hospitality industry campus recruiter and to provide meaningful data to college students who would be interviewing with these recruiters,” the author initially proffers. “Recruiting at the four-year hospitality program, by its nature, is not a science, nor is it highly quantifiable. The interviewing and selection processes are highly subjective and vary from company to company,” says Izzolo to preface his essay. “Data were collected via a questionnaire specifically designed to answer questions about the recruiters and/or the companies that sent interviewers to the placement office of the university's hospitality program,” our author says to explain the process used to gather information for the piece. Findings of the study indicate that the typical recruiter is male, college educated – but not necessarily in a Hospitality’ curriculum – and almost 80 percent of respondents said they had the authority to hire management trainees. Few campuses are visited by hospitality industry recruitment staff as evidenced by Izzolo’s observations/data. Table 3 analyzes the desirable traits a recruiter deems appropriate for the potential employee candidate. Personal appearance, work experience, grade point average, and verbal communication rank high on the list of distinguishable attributes. The most striking finding in this portion of the study is that a student’s GPA is virtually ignored. “Recruiting for the hospitality industry appears to be very subjective,” Izzolo says. “Recruiters are basing decisions to hire not on knowledge levels as determined by an academic grade point average but rather on criteria much less definitive, such as verbal skills and personal appearance,” our author opines. In closing, Izzolo concedes this is not a definitive study, but is merely a launching pad to a more comprehensive investigation on the recruitment subject.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.