Obtaining accurate frequencies of sequential patterns over a single sequence

In the mining and analysis of a single long sequence, one fundamental and important problem is obtaining accurate frequencies of sequential patterns over the sequence. However, we identify that five previous frequency measures suffer from inherent inaccuracies. To obtain more accurate frequencies, we introduce two basic principles called strict anti-monotonicity and maximum-count for frequency measures. Under the two principles, a new frequency measure is presented. An algorithm is also devised to compute it. Both theoretical analysis and empirical evaluation show that more accurate frequencies can be obtained under the new measure

Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.

Automatic pattern-taxonomy extraction for web mining

In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.

Single-room usage patterns and allocation decision-making in an Australian public hospital: a sequential exploratory study

AIMS AND OBJECTIVES: The aims are to (1) measure occupancy rates of single and shared rooms; (2) compare single room usage patterns and (3) explore the practice, rationale and decision-making processes associated with single rooms; across one Australian public health service.

BACKGROUND: There is a tendency in Australia and internationally to increase the proportion of single patient rooms in hospitals. To date there have been no Australian studies that investigate the use of single rooms in clinical practice.

DESIGN: This study used a sequential exploratory design with data collected in 2014.

METHODS: A descriptive survey was used to measure the use of single rooms across a two-week time frame. Semi-structured interviews were undertaken with occupancy decision-makers to explore the practices, rationale decision-making process associated with single-room allocation.

RESULTS: Total bed occupancy did not fall below 99·4% during the period of data collection. Infection control was the primary reason for patients to be allocated to a single room, however, the patterns varied according to ward type and single-room availability. For occupancy decision-makers, decisions about patient allocation was a complex and challenging process, influenced and complicated by numerous factors including occupancy rates, the infection status of the patient/s, funding and patient/family preference. Bed moves were common resulting from frequent re-evaluation of need.

CONCLUSION: Apart from infection control mandates, there was little tangible evidence to guide decision-making about single-room allocation. Further work is necessary to assist nurses in their decision-making.

RELEVANCE TO CLINICAL PRACTICE: There is a trend towards increasing the proportion of single rooms in new hospital builds. Coupled with the competing clinical demands for single room care, this study highlights the complexity of nursing decision-making about patient allocation to single rooms, an issue urgently requiring further attention.