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

Weaving Entities into Relations: From Page Retrieval to Relation Mining on the Web

With its sheer amount of information, the Web is clearly an important frontier for data mining. While Web mining must start with content on the Web, there is no effective ``search-based'' mechanism to help sifting through the information on the Web. Our goal is to provide a such online search-based facility for supporting query primitives, upon which Web mining applications can be built. As a first step, this paper aims at entity-relation discovery, or E-R discovery, as a useful function-- to weave scattered entities on the Web into coherent relations. To begin with, as our proposal, we formalize the concept of E-R discovery. Further, to realize E-R discovery, as our main thesis, we abstract tuple ranking-- the essential challenge of E-R discovery-- as pattern-based cooccurrence analysis. Finally, as our key insight, we observe that such relation mining shares the same core functions as traditional page-retrieval systems, which enables us to build the new E-R discovery upon today's search engines, almost for free. We report our system prototype and testbed, WISDM-ER, with real Web corpus. Our case studies have demonstrated a high promise, achieving 83%-91% accuracy for real benchmark queries-- and thus the real possibilities of enabling ad-hoc Web mining tasks with online E-R discovery

Query Chains: Learning to Rank from Implicit Feedback

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.Comment: 10 page

Relation Discovery from Web Data for Competency Management

This paper describes a technique for automatically discovering associations between people and expertise from an analysis of very large data sources (including web pages, blogs and emails), using a family of algorithms that perform accurate named-entity recognition, assign different weights to terms according to an analysis of document structure, and access distances between terms in a document. My contribution is to add a social networking approach called BuddyFinder which relies on associations within a large enterprise-wide "buddy list" to help delimit the search space and also to provide a form of 'social triangulation' whereby the system can discover documents from your colleagues that contain pertinent information about you. This work has been influential in the information retrieval community generally, as it is the basis of a landmark system that achieved overall first place in every category in the Enterprise Search Track of TREC2006