**5. Conclusion and research focus for future work**

This chapter has focused on the discussion of identifying inconsistencies associated with patterns. Even so, it has restricted its discussions to instances of contradictory data, deviations from standard data and missing values. Real life examples and open source datasets were used to illustrate our proposed approaches. The researchers anticipate that this interesting but understudied area of computing should be explored further by computer scientist to avoid instances of misinformation by our data analysts. Novel approaches for visual analysis of inconsistencies should be proposed. Also better means of diagrammatically visualising inconsistencies in pattern should be initiated.

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**Author details**

Ndoumbe Dora1

Nwagwu Honour Chika1

\*, Ukekwe Emmanuel1

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

and George Okereke1

1 University of Nigeria, Nsukka, Enugu State, Nigeria

\*Address all correspondence to: honour.nwagwu@unn.edu.ng

2 Nice diagnostic clinic Enugu State, Nigeria

provided the original work is properly cited.

, Ugwoke Celestine2

,

*Visual Identification of Inconsistency in Pattern DOI: http://dx.doi.org/10.5772/intechopen.95506* *Visual Identification of Inconsistency in Pattern DOI: http://dx.doi.org/10.5772/intechopen.95506*

*Applications of Pattern Recognition*

**Table 3.**

ConTra, Datax, and WellGrowth apps.

*Comparison of ConTra, Datax and WellGrowth apps.*

cies in pattern should be initiated.

**5. Conclusion and research focus for future work**

Six yardsticks were used in comparing the appropriateness of the explored tools and they include: pattern of missingness, amount of missingness, amount of contradiction, pattern of contradictory values, colour coding, and fault tolerance. ConTra and WellGrowth for example, does not mine missingness nor explore the pattern of missingness in a dataset. They do not measure the amount of missingness, unlike Datax that is designed to evaluate both the pattern and amount of missingness using Matrix Plot and bar charts respectively. It is evident from our discussions in this chapter, that ConTra and WellGrowth apps are used to explore inconsistencies notably contradictory data in established patterns of interest. In doing this, WellGrowth apps adopt colour coding and fault tolerance while Datax only adopts colour coding. **Table 3** depicts these discussed yardsticks for comparing

Pattern of missingness Amount of missingness Amount of contradiction Pattern of contradictory values Colour coding Fault tolerance

**ConTra Datax WellGrowth**

This chapter has focused on the discussion of identifying inconsistencies associated with patterns. Even so, it has restricted its discussions to instances of contradictory data, deviations from standard data and missing values. Real life examples and open source datasets were used to illustrate our proposed approaches. The researchers anticipate that this interesting but understudied area of computing should be explored further by computer scientist to avoid instances of misinformation by our data analysts. Novel approaches for visual analysis of inconsistencies should be proposed. Also better means of diagrammatically visualising inconsisten-

**38**
