Abstract: Distribution displays for categories are provided which illuminate the distribution of continuous attributes over all cases in a category, and which provide a histogram of the population of the different states of categorical attributes. An array of such displays by attribute (in one dimension) and category (in another dimension) may be provided. Category diagram displays are also provided for visualizing the different categories, and their distributions, populations, and similarities. These are displayed through different shading of nodes and edges representing categories and the relationship between two categories, and through proximity of nodes.

Abstract: Disclosed are systems and methods that facilitate spam detection and prevention at least in part by building or training filters using advanced IP address and/or URL features in connection with machine learning techniques. A variety of advanced IP address related features can be generated from performing a reverse IP lookup. Similarly, many different advanced URL based features can be created from analyzing at least a portion of any one URL detected in a message.

Abstract: The present invention involves a system and method that facilitate extracting data from messages for spam filtering. The extracted data can be in the form of features, which can be employed in connection with machine learning systems to build improved filters. Data associated with origination information as well as other information embedded in the body of the message that allows a recipient of the message to contact and/or respond to the sender of the message call be extracted as features. The features, or a subset thereof, can be normalized and/or deobfuscated prior to being employed as features of the machine learning systems. The (deobfuscated) features can be employed to populate a plurality of feature lists that facilitate spam detection and prevention. Exemplary features include an email address, an IP address, a URL, an embedded image pointing to a URL, and/or portions thereof.

Abstract: The subject invention provides for a system and method that facilitates detecting and preventing spam in a variety of networked communication environments. In particular, the invention provides several techniques for monitoring outgoing communications to identify potential spammers. Identification of potential spammers can be accomplished at least in part by a detection component that monitors per sender at least one of volume of outgoing messages, volume of recipients, and/or rate of outgoing messages. In addition, outgoing messages can be scored based at least in part on their content. The scores can be added per message per sender and if the total scores) per message or per sender exceeds some threshold, then further action can be taken to verify whether the potential spammer is a spammer. Such actions include human-inspecting a sample of the messages, sending challenges to the account, sending a legal notice to warn potential spammers and/or shutting down the account.

Abstract: The subject invention provides systems and methods that facilitate obfuscating a spam filtering system to hinder reverse engineering of the spam filters and/or to mitigate spammers from finding a message that consistently gets through the spam filters almost every time. The system includes a randomization component that randomizes a message score before the message is classified as spam or non-spam so as to obscure the functionality of the spam filter. Randomizing the message score can be accomplished in part by adding a random number or pseudo-random number to the message score before it is classified as spam or non-spam. The number added thereto can vary depending on at least one of several types of input such as time, user, message content, hash of message content, and hash of particularly important features of the message, for example. Alternatively, multiple spam filters can be deployed rather than a single best spam filter.

Abstract: The system and method of the present invention automatically assigns “scores” to the predictor/variable value pairs of a conventional probabilistic model to measure the relative impact or influence of particular elements of a set of topics, items, products, etc. in making specific predictions using the probabilistic model. In particular, these scores measure the relative impact, either positive or negative, that the value of each individual predictor variable has on the posterior distribution of the target topic, item, product, etc., for which a probability is being determined. These scores are useful for understanding why each prediction is make, and how much impact each predictor has on the prediction. Consequently, such scores are useful for explaining why a particular prediction or recommendation was made.

Abstract: The present invention is directed to a method and system for use in a computing environment to customize a filter utilized in classifying mail messages for a recipient. The present invention enables a recipient to reclassify a message that was previously classified by the filter, where the reclassification reflects the recipient's perspective of the class to which the message belongs. The reclassified messages are collectively stored in a training store. The information in the training store is then used to train the filter for future classifications, thus customizing the filter for the particular recipient. Further, the present invention is directed to adapting a filter to facilitate better detection and classification of spam over time by continuously retraining the filter. The retraining of the filter is an iterative process that utilizes previous spam fingerprints and message samples, to develop new spam fingerprints that are then utilized for the filtering process.

Abstract: A spelling correction system and method for phrasal strings using a dictionary looping technique. The method of the present invention includes spelling correction of an input phrasal string by segmenting the string into a plurality of different segmentations, comparing each segmentation to dictionary entries using the dictionary looping technique, assigning a cost to each segmentation, and determining an output string by finding the segmentation having the lowest cost. The dictionary looping technique determines compares each segmentation to dictionary entries by performing a looping search through a phrasal dictionary data structure in a looping manner whereby a number of different searches are performed. Dictionary looping allows the dictionary to be compact because the dictionary need not include all potential combinations of all possible phrases that could be encountered. The present invention also includes a phrasal spelling correction system and a dynamic dictionary that uses the above-described method.

Abstract: Optimizing storage and retrieval of monitoring data. In one aspect of this disclosure, there is a system, method and computer readable medium that stores instructions for instructing a computer system, to optimize storage and retrieval of data. In this embodiment, a transfer manager component acquires the data from an archive and assigns predetermined storage values to specified parameters that form the data structure of the acquired data. A database stores the data acquired by the transfer manager component in accordance with the predetermined storage values. A middle tier component extracts the data in the database and interpolates the data in accordance with the predetermined storage values.

Abstract: The system and method of the present invention automatically extracts the top k recommendations of objects, such as topics, items, products, books, movies, food, drinks, etc., from a local probabilistic recommendation system. Unlike prior systems, the present invention accomplishes the extraction of the top k recommendations of objects without examining a probability for every object that can be recommended. Further, the system and method of the present invention is capable of being implemented using probabilistic recommendation systems based on any conventional type of probabilistic distribution or machine learning technique, including, for example, decision trees and Bayesian networks.