Evaluation of Interestingness Measures for Ranking Discovered Knowledge

Hilderman, Robert J.; Hamilton, Howard J.

doi:10.1007/3-540-45357-1_28

Cited by 71 publications

(43 citation statements)

References 19 publications

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“…Many interestingness measures have been proposed and studied (Kamber & Shinghal, 1996;Hilderman & Hamilton, 2001;Tan, et al, 2002), each of them capturing different characteristics. In this section we give a brief overview of the most common quantitative measures and show how they can be used for mining arrangement rules.…”

Section: Interestingness Measuresmentioning

confidence: 99%

Mining frequent arrangements of temporal intervals

et al. 2009

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Abstract. The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Three efficient methods to find frequent arrangements of temporal intervals are described; the first two are tree-based and use breadth and depth first search to mine the set of frequent arrangements, whereas the third one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.

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Section: Interestingness Measuresmentioning

confidence: 99%

Mining frequent arrangements of temporal intervals

et al. 2009

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“…Hilderman and Hamilton (1999) discussed sixteen heuristic measures of interestingness which are based on diversity measures taken from different well-known areas such as statistics, ecology, information theory, and management. Hilderman and Hamilton (2001) mentioned that these measures evaluate the distribution within a tuple and assign …”

Section: Heuristic Measures Of Interestingnessmentioning

confidence: 99%

“…Adding these three measures to the five ones presented by Hilderman and Hamilton (2001), we end up with a set of 8 measures that are theoretically acceptable for such a task. Table 7 summarizes the status of all interestingness measures mentioned with respect to the five principles, where a "×" means that a measure satisfies the respective principle.…”

Section: Principle P5mentioning

confidence: 99%

“…Table 7 summarizes the status of all interestingness measures mentioned with respect to the five principles, where a "×" means that a measure satisfies the respective principle. In Table 7, we added the properties of the three new measures as compared to the table presented by Hilderman and Hamilton (2001).…”

Section: Principle P5mentioning

confidence: 99%

“…By using the input data of Table 8 which is used previously by Hilderman and Hamilton (2001), we analyzed the statistical distribution of the indices values produced by the 19 measures. The input data used here consists of 16928 vectors which are the list of all possible ordered arrangements of a set of 50 objects among 10 classes.…”

Section: Evaluation Of the Interestingness Measuresmentioning

confidence: 99%

See 2 more Smart Citations

On Mining Summaries by Objective Measures of Interestingness

2006

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Abstract. Knowledge discovery in databases is used to discover useful and understandable knowledge from large databases. A process of knowledge discovery consists of two steps, the data mining step and the evaluation step. In this paper, evaluating and ranking the interestingness of summaries generated from databases, which is a part of the second step, is studied using diversity measures. Sixteen previously analyzed diversity measures of interestingness are used along with three not previously considered ones, brought from different well-known areas. The latter three measures are evaluated theoretically according to five principles that a measure must satisfy to be qualified acceptable for ranking summaries. A theoretical correlation study between the eight measures that satisfy all five principles is presented based on mathematical proofs. An empirical evaluation is conducted using three real databases. Then, a classification of the eight measures is deduced. The resulting classification is used to reduce the number of measures to only two, which are the best over all criteria, and that produce non-similar results. This helps the user interpret the most important discovered knowledge in his decision making process.

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Using importance flooding to identify interesting networks of criminal activity

Marshall

Chen

2008

J. Am. Soc. Inf. Sci.

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Effectively harnessing available data to support homeland‐security‐related applications is a major focus in the emerging science of intelligence and security informatics (ISI). Many studies have focused on criminal‐network analysis as a major challenge within the ISI domain. Though various methodologies have been proposed, none have been tested for usefulness in creating link charts. This study compares manually created link charts to suggestions made by the proposed importance‐flooding algorithm. Mirroring manual investigational processes, our iterative computation employs association‐strength metrics, incorporates path‐based node importance heuristics, allows for case‐specific notions of importance, and adjusts based on the accuracy of previous suggestions. Interesting items are identified by leveraging both node attributes and network structure in a single computation. Our data set was systematically constructed from heterogeneous sources and omits many privacy‐sensitive data elements such as case narratives and phone numbers. The flooding algorithm improved on both manual and link‐weight‐only computations, and our results suggest that the approach is robust across different interpretations of the user‐provided heuristics. This study demonstrates an interesting methodology for including user‐provided heuristics in network‐based analysis, and can help guide the development of ISI‐related analysis tools.

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Evaluation of Interestingness Measures for Ranking Discovered Knowledge

Cited by 71 publications

References 19 publications

Mining frequent arrangements of temporal intervals

Mining frequent arrangements of temporal intervals

On Mining Summaries by Objective Measures of Interestingness

Using importance flooding to identify interesting networks of criminal activity

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