A Model-Based Frequency Constraint for Mining Associations from Transaction Data

Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an association, is used as the primary indicator of the associations's significance. A single user-specified support threshold is used to decided if associations should be further investigated. Support has some known problems with rare items, favors shorter itemsets and sometimes produces misleading associations. In this paper we develop a novel model-based frequency constraint as an alternative to a single, user-specified minimum support. The constraint utilizes knowledge of the process generating transaction data by applying a simple stochastic mixture model (the NB model) which allows for transaction data's typically highly skewed item frequency distribution. A user-specified precision threshold is used together with the model to find local frequency thresholds for groups of itemsets. Based on the constraint we develop the notion of NB-frequent itemsets and adapt a mining algorithm to find all NB-frequent itemsets in a database. In experiments with publicly available transaction databases we show that the new constraint provides improvements over a single minimum support threshold and that the precision threshold is more robust and easier to set and interpret by the user

Exploring the Evolution of Node Neighborhoods in Dynamic Networks

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes

CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams

Mining association rules from data streams is a challenging task due to the (typically) limited resources available vs. the large size of the result. Frequent closed itemsets (FCI) enable an efficient first step, yet current FCI stream miners are not optimal on resource consumption, e.g. they store a large number of extra itemsets at an additional cost. In a search for a better storage-efficiency trade-off, we designed Ciclad,an intersection-based sliding-window FCI miner. Leveraging in-depth insights into FCI evolution, it combines minimal storage with quick access. Experimental results indicate Ciclad's memory imprint is much lower and its performances globally better than competitor methods.Comment: KDD2

Colossal Trajectory Mining: A unifying approach to mine behavioral mobility patterns

Spatio-temporal mobility patterns are at the core of strategic applications such as urban planning and monitoring. Depending on the strength of spatio-temporal constraints, different mobility patterns can be defined. While existing approaches work well in the extraction of groups of objects sharing fine-grained paths, the huge volume of large-scale data asks for coarse-grained solutions. In this paper, we introduce Colossal Trajectory Mining (CTM) to efficiently extract heterogeneous mobility patterns out of a multidimensional space that, along with space and time dimensions, can consider additional trajectory features (e.g., means of transport or activity) to characterize behavioral mobility patterns. The algorithm is natively designed in a distributed fashion, and the experimental evaluation shows its scalability with respect to the involved features and the cardinality of the trajectory dataset

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed

Mining XML Documents

XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the increasing size of XML collections as information sources, mining techniques that traditionally exist for text collections or databases need to be adapted and new methods to be invented to exploit the particular structure of XML documents. Basically XML documents can be seen as trees, which are well known to be complex structures. This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. We focus on three mining tasks: classification and clustering which are standard for text collections; discovering of frequent tree structure which is especially important for heterogeneous collection. This chapter presents some recent approaches and algorithms to support these tasks together with experimental evaluation on a variety of large XML collections

Data mining techniques for complex application domains

The emergence of advanced communication techniques has increased availability of large collection of data in electronic form in a number of application domains including healthcare, e- business, and e-learning. Everyday a large amount of records are stored electronically. However, finding useful information from such a large data collection is a challenging issue. Data mining technology aims automatically extracting hidden knowledge from large data repositories exploiting sophisticated algorithms. The hidden knowledge in the electronic data may be potentially utilized to facilitate the procedures, productivity, and reliability of several application domains. The PhD activity has been focused on novel and effective data mining approaches to tackle the complex data coming from two main application domains: Healthcare data analysis and Textual data analysis. The research activity, in the context of healthcare data, addressed the application of different data mining techniques to discover valuable knowledge from real exam-log data of patients. In particular, efforts have been devoted to the extraction of medical pathways, which can be exploited to analyze the actual treatments followed by patients. The derived knowledge not only provides useful information to deal with the treatment procedures but may also play an important role in future predictions of potential patient risks associated with medical treatments. The research effort in textual data analysis is twofold. On the one hand, a novel approach to discovery of succinct summaries of large document collections has been proposed. On the other hand, the suitability of an established descriptive data mining to support domain experts in making decisions has been investigated. Both research activities are focused on adopting widely exploratory data mining techniques to textual data analysis, which require overcoming intrinsic limitations for traditional algorithms for handling textual documents efficiently and effectively