Hub neighbors require much more data to be. Although compressed-sensing based methods require much less data than conventional approaches, the compressed sensing for reconstructing heterogeneous networks has not been fully exploited because of hubs.
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Recent years have witnessed a rapid development of network reconstruction approaches, especially for a series of methods based on compressed sensing. 1 Introduction The idea that the relative positions of query terms within a document may supply information about relevance arose nearly forty years ago. Consideration is given to the practical application of the formalism to both locating and scoring concept intersections and to locating phrases (with an estimate of confidence) despite intervening or substituted words. Based on the formalism, approaches to issues such as scoring partial spans, treatment of repeated term occurrences within spans, and the importance of ordering are proposed. A theoretical framework for lexical spans is now proposed which encompasses these approaches and suggests a number of important directions for future experimental work. A major benefit of this approach is that relevance scoring does not rely on collection frequency statistics. Recent work has achieved promising retrieval performance using distance between term occurrences as a primary estimator of document relevance. Section 4 presents our experimental setup and re- sults, Section 5 discusses related work, and Section 6 closes with conclusions and recommendations. Section 3 introduces community detection and its application to mar- ket basket analysis, including our utility measure for com- munities. Section 2 discusses product networks and their properties. The rest of the paper is organized as follows. Our results are based on data from a University convenience store consisting of over 660,000 transactions involving over 2,200 products. We evaluate our methods both comparatively and financially, and conclude that community detection provides a valuable supplement to association rules. can quickly isolate important relationships. All our conclusions are validated on real store data. We develop a novel utility mea- sure for communities of products and show, both financially and intuitively, that community detection provides a useful complement to association rules for market basket analysis. One can then discover com- munities of products that are bought together, which can lead to expressive exploration and discovery of a larger in- fluence zone of product(s). Departing from the standard approach of frequent itemset mining, we posit that purchase data can be modeled as a social network. This purchase data of products can shed important light on how product(s) might influence sales of other product(s). A market basket is a set of products that form a single retail transaction.
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