A Review on: Efficient Method for Mining Frequent Itemsets on Temporal Data

Temporal data can hold time-stamped information that affects the results of data mining. Customary strategies for finding frequent itemsets accept that datasets are static; also the instigated rules are relevant over the whole dataset. In any case, this is not the situation when data is temporal. The work is done to enhance the proficiency of mining frequent itemsets on temporal data. The patterns can hold in either all or, then again a portion of the intervals. It proposes another method with respect to time interval is called as frequent itemsets mining with time cubes. The concentration is building up an efficient algorithm for this mining issue by broadening the notable a priori algorithm. The thought of time cubes is proposed to handle different time hierarchies. This is the route by which the patterns that happen intermittently, amid a time interval or both, are perceived. Another thickness limit is likewise proposed to take care of the overestimating issue of time periods and furthermore ensure that found patterns are valid

All in a twitter: Self-tuning strategies for a deeper understanding of a crisis tweet collection

Natural disasters have become more frequent during the past 20 years due to significant climate changes. These natural events are hotly debated on social networks like Twitter and a huge amount of short text messages are continuously and promptly exchanged with personal opinions, descriptions of the natural events and their corresponding consequences. The analysis of these large and complex data could help policy-makers to better understand the event as well as to set priorities. However, the correct configuration of the tweet mining process is still challenging due to variable data distribution and the availability of a large number of algorithms with different specific parameters. The analyst need to perform a large number of experiments to identify the best configuration for the overall knowledge discovery process. Innovative, scalable, and parameter-free solutions need to be explored to streamline the analytics process. This paper presents an enhanced version of PASTA (a distributed self-tuning engine) applied to a crisis tweet collection to group a corpus of tweets into cohesive and well-separated clusters with minimal analyst intervention. Experimental results performed on real data collected during natural disasters show the effectiveness of PASTA in discovering interesting groups of correlated tweets without selecting neither the algorithms nor their parameters

Identifying collaborations among researchers: a pattern-based approach

In recent years a huge amount of publications and scientific reports has become available through digital libraries and online databases. Digital libraries commonly provide advanced search interfaces, through which researchers can find and explore the most related scientific studies. Even though the publications of a single author can be easily retrieved and explored, understanding how authors have collaborated with each other on specific research topics and to what extent their collaboration have been fruitful is, in general, a challenging task. This paper proposes a new pattern-based approach to analyzing the correlations among the authors of most influential research studies. To this purpose, it analyzes publication data retrieved from digital libraries and online databases by means of an itemset-based data mining algorithm. It automatically extracts patterns representing the most relevant collaborations among authors on specific research topics. Patterns are evaluated and ranked according to the number of citations received by the corresponding publications. The proposed approach was validated in a real case study, i.e., the analysis of scientific literature on genomics. Specifically, we first analyzed scientific studies on genomics acquired from the OMIM database to discover correlations between authors and genes or genetic disorders. Then, the reliability of the discovered patterns was assessed using the PubMed search engine. The results show that, for the majority of the mined patterns, the most influential (top ranked) studies retrieved by performing author-driven PubMed queries range over the same gene/genetic disorder indicated by the top ranked pattern

Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications

Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies

First Elements on Knowledge Discovery guided by Domain Knowledge (KDDK)

International audienceIn this paper, we present research trends carried out in the Orpailleur team at Loria, showing how knowledge discovery and knowledge processing may be combined. The knowledge discovery in databases process (KDD) consists in processing a huge volume of data for extracting significant and reusable knowledge units. From a knowledge representation perspective, the KDD process may take advantage of domain knowledge embedded in ontologies relative to the domain of data, leading to the notion of ''knowledge discovery guided by domain knowledge'' or KDDK. The KDDK process is based on the classification process (and its multiple forms), e.g. for modeling, representing, reasoning, and discovering. Some applications are detailed, showing how KDDK can be instantiated in an application domain. Finally, an architecture of an integrated KDDK system is proposed and discussed