We present a new approach to jet definition as an alternative to methods that exploit kinematic data directly, such as the anti-$k_T$ scheme; we use the kinematics to represent the particles in an event in a new multidimensional space. The latter is constituted by the eigenvectors of a matrix of kinematic relations between particles, and the resulting partition is called spectral clustering. After confirming its Infra-Red (IR) safety, we compare its performance to the anti-$k_T$ algorithm in reconstructing relevant final states. We base this on Monte Carlo (MC) samples generated from the following processes: $(ggto H_{125,text{GeV}} rightarrow H_{40,text{GeV}} H_{40,text{GeV}} rightarrow b bar{b} b bar{b}), (ggto H_{500,text{GeV}} rightarrow H_{125,text{GeV}} H_{125,text{GeV}} rightarrow b bar{b} b bar{b})$ and $(gg,qbar qto tbar tto bbar b W^+W^-to bbar b jj ell u_ell)$. Finally, we show that the results for spectral clustering are obtained without any change in the algorithms parameter settings, unlike the anti-$k_T$ case, which requires the cone size to be adjusted to the physics process under study.
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