Churn is the movement of customers from one mobile network operator to another. It is always better to retain a customer than having to find a new customer in the present competitive environment and the importance of this fact can’t be stressed enough. Being more of a social phenomenon than a mathematical one the existing models fail in prediction of such a behavioral quantity. Churn prediction is valuable to the mobile operator depending on the level of accuracy of predictions. This paper presents predictive modeling of customer behavior based on the application of hybrid learning approaches for churn prediction in the mobile network. Our proposed framework deals with a better and more accurate churner prediction technique compared to the existing ones as it incorporates hybrid learning method which is a combination of tree induction system and genetic programming to derive the rules for classification based on the customer behavior. Finally using the game theory techniques we understand the community effect of churn. We calculated the predicted score which is a churn value of a mobile customer. The proposed model is used for prediction of various user defined groupings based on usage time, location and their underlying social network, thus making it a pragmatic approach which models churn on human level than a mathematical level. The post evaluation results on a real world dataset from a leading operator validate our findings.

The New York Public Library is participating in the Chronicling America initiative to develop an online searchable database of historically significant newspaper articles. Microfilm copies of the papers are scanned and high resolution OCR software is run on them. The text from the OCR provides a wealth of data and opinion for researchers and historians. However, the categorization of articles provided by the OCR engine is rudimentary and a large number of the articles are labeled ``editorial" without further categorization. To provide a more refined grouping of articles, unsupervised machine learning algorithms (such as K-Means) are being investigated. The K-Means algorithm requires tuning of parameters such as the number of clusters and mechanism of seeding to ensure that the search is not prone to being caught in a local minima. We designed a pilot study to observe whether humans are adept at finding sub-categories. The subjective labels provided by humans are used as a guide to compare performance of the automated clustering techniques. In addition, seeds provided by annotators are carefully incorporated into a semi-supervised K-Means algorithm (Seeded K-Means); empirical results indicate that this helps to improve performance and provides an intuitive sub-categorization of the articles labeled ``editorial" by the OCR engine.

Community mining is applied in order to identify groups of users which share, e.g., common interests or expertise. This paper presents an approach for mining descriptive patterns in order to characterize communities in terms of their distinctive features: For an efficient discovery approach, we introduce optimistic estimates for obtaining an upper bound for the community quality. We present an evaluation using data from the real-world social bookmarking system BibSonomy.

The filter model of feature selection has been well studied. In previous studies, classification performance has traditionally been proposed as a way to evaluate filter solutions. In this study, a new method of comparing feature ranking techniques is presented providing a straightforward approach for quantifying individual filters’ robustness to class noise. Six commonly-used filters, plus one which is rarely used, are investigated regarding their ability to retain, in the presence of class noise, strong classification performance. Three classifiers and one classification performance metric are considered. The experimental results of this study show that Gain Ratio, one of the well known and widely used filters, is very sensitive to class noise. ReliefF offers the best results with both the NB and kNN learners while Signal-to-noise, though not as widely used in the literature as the others, outperforms all the filters with the SVM learner.

Data mining is the process of extracting hidden patterns from data. With data ever increasing in volume, mining it into usable information is becoming increasingly important. Data mining approaches are commonly used in a wide range of profiling services, including marketing, fraud detection, and scientific discovery. The FLAIRS Data Mining special track is devoted to data mining with the aim of presenting new and important contributions in this area.

We futher claim that categorization within this domain is a recursive process of subdivision. We describe a theory of perceptual understanding, implemented Despite its central role in our theory, categorization isn't in Mathematica, that forms rich representations of the only way people demonstrate understanding of exemplar very simple visual concepts. We suggest that this theory can sets. In order to accommodate this, our theory supports a variety represent any category distinction that a human is liable to of other operations, such as outlier detection and concept make within its limited domain.

In this paper, we show how to make a cognitive tutoring agent capable of precise causal reasoning by integrating constraints with data mining algorithms. Putting constraints on recorded interactions between the agent and learners during learning activities allows data mining algorithms to extract the causes of the learners’ problems. Subsequently, the agent uses this information to provide useful and customized explanations to learners.

We have experimented with the notion of — conceptualization, and contextualization from situated cognition and psychic reflection from activity theory for identifying context into a method called the activity states framework (ASF). The purpose of the ASF is to provide a method of analysis for identifying collaborators activity during situated context − specific to Web-mediated collaboration. This paper introduces the ASF.

In this paper, we describe a framework dedicated to studies and experimentations upon the nature of the relationships between the rational reasoning process of an artificial agent and its psychological counterpart, namely its behavioral reasoning process. This study is focused on the domain of Conversational Assistant Agents, which are software tools providing various kinds of assistance to people of the general public interacting with computer-based applications or services. In this context, we show on some examples the need for the agents to be able to exhibit both a rational reasoning about the system functioning and a human-like believable dialogical interaction with the users.

We discuss and evaluate metrics for difficulty rating of Sudoku puzzles. The correlation coefficient with human performance for our best metric is 0.95. The data on human performance were obtained from three web portals and they comprise thousands of hours of human solving over 2000 problems. We provide a simple computational model of human solving activity and evaluate it over collected data. Using the model we show that there are two sources of problem difficulty: complexity of individual steps (logic operations) and structure of dependency among steps. Beside providing a very good Sudoku-tuned metric, we also discuss a metric with few Sudoku-specific details, which still provides good results (correlation coefficient is 0.88). Hence we believe that the approach should be applicable to difficulty rating of other constraint satisfaction problems.