Iterative Structure Discovery in Graph-Based Data

Abstract

Received Revised AcceptedMuch of current data mining research is focused on discovering sets of attributes that discriminate data entities into classes, such as shopping trends for a particular demographic group. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event-driven or relationally structured. In this paper we present approaches to address two related challenges; the need to assimilate incremental data updates and the need to mine monolithic datasets. Many realistic problems are continuous in nature and therefore require a data mining approach that can evolve discovered knowledge over time. Similarly, many problems present data sets that are too large to fit into dynamic memory on conventional computer systems. We address incremental data mining by introducing a mechanism for summarizing discoveries from previous data increments so that the globally-best patterns can be computed by mining only the new data increment. To address monolithic datasets we introduce a technique by which these datasets can be partitioned and mined serially with minimal impact on the result quality. We present applications of our work in both the counter-terrorism and bioinformatics domains.

Automated Visualization of Rule-based Models

“…Network motifs such as those shown in Fig 12 could be identified by cycle detection [51]. Groups of rules and atomic patterns on the regulatory graph could be inferred by structure discovery methods for graph data [52,53]. Thus, the rule-derived regulatory graph paves the way for novel applications of graph analysis, data mining and machine learning to rule-based models.…”

Concept-based learning of human behavior for customer relationship management

“…There exists a number of settings in which graph-based data mining and clustering is performed (e.g., [26,7]) that rely on information-theoretic or error-based measures. Concepts are the basic units of thought that underlie human intelligence and communication.…”

A novel approach for classifying customer complaints through graphs similarities in argumentative dialogues

“…The family of these concepts obeys the mathematical axioms defining a lattice, and is called a concept lattice or Galois lattice. 7 So called line diagrams [43] are used in order to succinctly represent information about intents and extents of formal context in a concept lattice. Nodes are circles that can be labelled with a) both attributes and objects; b) attributes; c) objects or d) none.…”

“…It is used to verify that the attributes of communicative actions have been selected properly (i.e. that the space of meanings 7 An in-depth discussion of the underlying definitions and properties of formal concept analysis is outside the scope of this article. For more details see [23].…”